Skip-gram word2vec

In this notebook, I'll lead you through using TensorFlow to implement the word2vec algorithm using the skip-gram architecture. By implementing this, you'll learn about embedding words for use in natural language processing. This will come in handy when dealing with things like machine translation.

Readings

Here are the resources I used to build this notebook. I suggest reading these either beforehand or while you're working on this material.

A really good conceptual overview of word2vec from Chris McCormick
First word2vec paper from Mikolov et al.
NIPS paper with improvements for word2vec also from Mikolov et al.
An implementation of word2vec from Thushan Ganegedara
TensorFlow word2vec tutorial

Word embeddings

When you're dealing with words in text, you end up with tens of thousands of classes to predict, one for each word. Trying to one-hot encode these words is massively inefficient, you'll have one element set to 1 and the other 50,000 set to 0. The matrix multiplication going into the first hidden layer will have almost all of the resulting values be zero. This a huge waste of computation.

To solve this problem and greatly increase the efficiency of our networks, we use what are called embeddings. Embeddings are just a fully connected layer like you've seen before. We call this layer the embedding layer and the weights are embedding weights. We skip the multiplication into the embedding layer by instead directly grabbing the hidden layer values from the weight matrix. We can do this because the multiplication of a one-hot encoded vector with a matrix returns the row of the matrix corresponding the index of the "on" input unit.

Instead of doing the matrix multiplication, we use the weight matrix as a lookup table. We encode the words as integers, for example "heart" is encoded as 958, "mind" as 18094. Then to get hidden layer values for "heart", you just take the 958th row of the embedding matrix. This process is called an embedding lookup and the number of hidden units is the embedding dimension.

There is nothing magical going on here. The embedding lookup table is just a weight matrix. The embedding layer is just a hidden layer. The lookup is just a shortcut for the matrix multiplication. The lookup table is trained just like any weight matrix as well.

Embeddings aren't only used for words of course. You can use them for any model where you have a massive number of classes. A particular type of model called Word2Vec uses the embedding layer to find vector representations of words that contain semantic meaning.

Word2Vec

The word2vec algorithm finds much more efficient representations by finding vectors that represent the words. These vectors also contain semantic information about the words. Words that show up in similar contexts, such as "black", "white", and "red" will have vectors near each other. There are two architectures for implementing word2vec, CBOW (Continuous Bag-Of-Words) and Skip-gram.

In this implementation, we'll be using the skip-gram architecture because it performs better than CBOW. Here, we pass in a word and try to predict the words surrounding it in the text. In this way, we can train the network to learn representations for words that show up in similar contexts.

First up, importing packages.



In [1]:

    
import time

import numpy as np
import tensorflow as tf

import utils

Load the text8 dataset, a file of cleaned up Wikipedia articles from Matt Mahoney. The next cell will download the data set to the data folder. Then you can extract it and delete the archive file to save storage space.



In [2]:

    
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import zipfile

dataset_folder_path = 'data'
dataset_filename = 'text8.zip'
dataset_name = 'Text8 Dataset'

class DLProgress(tqdm):
    last_block = 0

    def hook(self, block_num=1, block_size=1, total_size=None):
        self.total = total_size
        self.update((block_num - self.last_block) * block_size)
        self.last_block = block_num

if not isfile(dataset_filename):
    with DLProgress(unit='B', unit_scale=True, miniters=1, desc=dataset_name) as pbar:
        urlretrieve(
            'http://mattmahoney.net/dc/text8.zip',
            dataset_filename,
            pbar.hook)

if not isdir(dataset_folder_path):
    with zipfile.ZipFile(dataset_filename) as zip_ref:
        zip_ref.extractall(dataset_folder_path)
        
with open('data/text8') as f:
    text = f.read()









    



Text8 Dataset: 31.4MB [00:23, 1.33MB/s]

Preprocessing

Here I'm fixing up the text to make training easier. This comes from the utils module I wrote. The preprocess function coverts any punctuation into tokens, so a period is changed to <PERIOD>. In this data set, there aren't any periods, but it will help in other NLP problems. I'm also removing all words that show up five or fewer times in the dataset. This will greatly reduce issues due to noise in the data and improve the quality of the vector representations. If you want to write your own functions for this stuff, go for it.



In [3]:

    
words = utils.preprocess(text)
print(words[:30])









    



['anarchism', 'originated', 'as', 'a', 'term', 'of', 'abuse', 'first', 'used', 'against', 'early', 'working', 'class', 'radicals', 'including', 'the', 'diggers', 'of', 'the', 'english', 'revolution', 'and', 'the', 'sans', 'culottes', 'of', 'the', 'french', 'revolution', 'whilst']



In [4]:

    
print("Total words: {}".format(len(words)))
print("Unique words: {}".format(len(set(words))))









    



Total words: 16680599
Unique words: 63641

And here I'm creating dictionaries to convert words to integers and backwards, integers to words. The integers are assigned in descending frequency order, so the most frequent word ("the") is given the integer 0 and the next most frequent is 1 and so on. The words are converted to integers and stored in the list int_words.



In [5]:

    
vocab_to_int, int_to_vocab = utils.create_lookup_tables(words)
int_words = [vocab_to_int[word] for word in words]



In [6]:

    
print(len(int_words))
print(int_words[0:10])









    



16680599
[5239, 3082, 11, 5, 194, 1, 3135, 45, 58, 155]

Subsampling

Words that show up often such as "the", "of", and "for" don't provide much context to the nearby words. If we discard some of them, we can remove some of the noise from our data and in return get faster training and better representations. This process is called subsampling by Mikolov. For each word $w_i$ in the training set, we'll discard it with probability given by

$$ P(w_i) = 1 - \sqrt{\frac{t}{f(w_i)}} $$

where $t$ is a threshold parameter and $f(w_i)$ is the frequency of word $w_i$ in the total dataset.

I'm going to leave this up to you as an exercise. This is more of a programming challenge, than about deep learning specifically. But, being able to prepare your data for your network is an important skill to have. Check out my solution to see how I did it.

Exercise: Implement subsampling for the words in int_words. That is, go through int_words and discard each word given the probablility $P(w_i)$ shown above. Note that $P(w_i)$ is the probability that a word is discarded. Assign the subsampled data to train_words.



In [7]:

    
## Your code here
import math
from collections import Counter
threshold = 0.001
count_word_occurences = Counter(int_words)
frequencies = {word_id: count_word_occurences[word_id]/len(int_words) for word_id in count_word_occurences}
proba_to_discard = {word_id : 1-math.sqrt(threshold/frequencies[word_id]) for word_id in count_word_occurences}



In [8]:

    
print(len(proba_to_discard))
print(len(count_word_occurences))
print(frequencies[3])
print(proba_to_discard[3])
for i in range(50000,55000):
    print(frequencies[i],proba_to_discard[i])









    



63641
63641
0.02468520465002486
0.7987288008960663
5.395489694344909e-07 -42.051131098832805
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In [9]:

    
import random

train_words = [word for word in int_words if random.uniform(0,1)>proba_to_discard[word]]# The final subsampled word list



In [10]:

    
len(train_words)









    Out[10]:





11660814

Making batches

Now that our data is in good shape, we need to get it into the proper form to pass it into our network. With the skip-gram architecture, for each word in the text, we want to grab all the words in a window around that word, with size $C$.

From Mikolov et al.:

"Since the more distant words are usually less related to the current word than those close to it, we give less weight to the distant words by sampling less from those words in our training examples... If we choose $C = 5$, for each training word we will select randomly a number $R$ in range $< 1; C >$, and then use $R$ words from history and $R$ words from the future of the current word as correct labels."

Exercise: Implement a function get_target that receives a list of words, an index, and a window size, then returns a list of words in the window around the index. Make sure to use the algorithm described above, where you choose a random number of words from the window.



In [143]:

    
def get_target(words, idx, window_size=5):
    ''' Get a list of words in a window around an index. '''
    
    # Your code here
    R = random.randint(0,window_size)
    
    return words[max(idx-R,0):min(len(words),idx+R)]

Here's a function that returns batches for our network. The idea is that it grabs batch_size words from a words list. Then for each of those words, it gets the target words in the window. I haven't found a way to pass in a random number of target words and get it to work with the architecture, so I make one row per input-target pair. This is a generator function by the way, helps save memory.



In [144]:

    
def get_batches(words, batch_size, window_size=5):
    ''' Create a generator of word batches as a tuple (inputs, targets) '''
    # print("get_batches", batch_size, len(words))
    n_batches = len(words)//batch_size
    
    # only full batches
    words = words[:n_batches*batch_size]
    
    for idx in range(0, len(words), batch_size):
        x, y = [], []
        batch = words[idx:idx+batch_size]
        for ii in range(len(batch)):
            batch_x = batch[ii]
            batch_y = get_target(batch, ii, window_size)
            #print("get target", ii  )
            #print("get target  (" + batch_x + "," + batch_y+ ")" )
            
            y.extend(batch_y)
            x.extend([batch_x]*len(batch_y))
        
        yield x, y

Building the graph

From Chris McCormick's blog, we can see the general structure of our network.

The input words are passed in as integers. This will go into a hidden layer of linear units, then into a softmax layer. We'll use the softmax layer to make a prediction like normal.

The idea here is to train the hidden layer weight matrix to find efficient representations for our words. We can discard the softmax layer becuase we don't really care about making predictions with this network. We just want the embedding matrix so we can use it in other networks we build from the dataset.

I'm going to have you build the graph in stages now. First off, creating the inputs and labels placeholders like normal.

Exercise: Assign inputs and labels using tf.placeholder. We're going to be passing in integers, so set the data types to tf.int32. The batches we're passing in will have varying sizes, so set the batch sizes to [None]. To make things work later, you'll need to set the second dimension of labels to None or 1.



In [145]:

    
train_graph = tf.Graph()
with train_graph.as_default():
    inputs = tf.placeholder(tf.int32, name = "inputs")
    labels = tf.placeholder(tf.float32,shape=[None,None],name = "labels")

Embedding

The embedding matrix has a size of the number of words by the number of units in the hidden layer. So, if you have 10,000 words and 300 hidden units, the matrix will have size $10,000 \times 300$. Remember that we're using tokenized data for our inputs, usually as integers, where the number of tokens is the number of words in our vocabulary.

Exercise: Tensorflow provides a convenient function tf.nn.embedding_lookup that does this lookup for us. You pass in the embedding matrix and a tensor of integers, then it returns rows in the matrix corresponding to those integers. Below, set the number of embedding features you'll use (200 is a good start), create the embedding matrix variable, and use tf.nn.embedding_lookup to get the embedding tensors. For the embedding matrix, I suggest you initialize it with a uniform random numbers between -1 and 1 using tf.random_uniform.



In [146]:

    
n_vocab = len(int_to_vocab)
n_embedding = 200 # Number of embedding features 
with train_graph.as_default():
    embedding = tf.Variable(
                        tf.random_uniform(
                                [n_vocab,n_embedding],
                                minval=-1,
                                maxval=1)
                            )
 # create embedding weight matrix here
    embed = tf.nn.embedding_lookup(embedding, inputs)# use tf.nn.embedding_lookup to get the hidden layer output

Negative sampling

For every example we give the network, we train it using the output from the softmax layer. That means for each input, we're making very small changes to millions of weights even though we only have one true example. This makes training the network very inefficient. We can approximate the loss from the softmax layer by only updating a small subset of all the weights at once. We'll update the weights for the correct label, but only a small number of incorrect labels. This is called "negative sampling". Tensorflow has a convenient function to do this, tf.nn.sampled_softmax_loss.

Exercise: Below, create weights and biases for the softmax layer. Then, use tf.nn.sampled_softmax_loss to calculate the loss. Be sure to read the documentation to figure out how it works.



In [147]:

    
# Number of negative labels to sample
n_sampled = 100
with train_graph.as_default():
    softmax_w = tf.Variable(
                            tf.random_normal(
                                [n_vocab,n_embedding],stddev=0.01)
                           ) 
     
# create softmax weight matrix here
    softmax_b = tf.Variable(
                                tf.zeros(n_vocab)
                            )# create softmax biases here
    
    # Calculate the loss using negative sampling
    loss = tf.nn.sampled_softmax_loss(
                    softmax_w,
                    softmax_b,
                    labels,
                    inputs=embed,
                    num_sampled=n_sampled,
                    num_classes=n_vocab,
                    )
    
    cost = tf.reduce_mean(loss)
    optimizer = tf.train.AdamOptimizer().minimize(cost)

Validation

This code is from Thushan Ganegedara's implementation. Here we're going to choose a few common words and few uncommon words. Then, we'll print out the closest words to them. It's a nice way to check that our embedding table is grouping together words with similar semantic meanings.



In [152]:

    
with train_graph.as_default():
    ## From Thushan Ganegedara's implementation
    valid_size = 16 # Random set of words to evaluate similarity on.
    valid_window = 100
    # pick 8 samples from (0,100) and (1000,1100) each ranges. lower id implies more frequent 
    valid_examples = np.array(random.sample(range(valid_window), valid_size//2))
    valid_examples = np.append(valid_examples, 
                               random.sample(range(1000,1000+valid_window), valid_size//2))

    valid_dataset = tf.constant(valid_examples, dtype=tf.int32)
    
    # We use the cosine distance:
    norm = tf.sqrt(tf.reduce_sum(tf.square(embedding), 1, keep_dims=True))
    normalized_embedding = embedding / norm
    valid_embedding = tf.nn.embedding_lookup(normalized_embedding, valid_dataset)
    similarity = tf.matmul(valid_embedding, tf.transpose(normalized_embedding))



In [153]:

    
# If the checkpoints directory doesn't exist:
!mkdir checkpoints









    



mkdir: cannot create directory ‘checkpoints’: File exists

Training

Below is the code to train the network. Every 100 batches it reports the training loss. Every 1000 batches, it'll print out the validation words.



In [150]:

    
epochs = 10
batch_size = 1000
window_size = 10

with train_graph.as_default():
    saver = tf.train.Saver()

with tf.Session(graph=train_graph) as sess:
    iteration = 1
    loss = 0
    sess.run(tf.global_variables_initializer())

    for e in range(1, epochs+1):
        batches = get_batches(train_words, batch_size, window_size)
        
        start = time.time()
        for x, y in batches:
            
            feed = {inputs: x,
                    labels: np.array(y)[:, None]}
            train_loss, _ = sess.run([cost, optimizer], feed_dict=feed)
            
            loss += train_loss
            
            if iteration % 100 == 0: 
                end = time.time()
                print("Epoch {}/{}".format(e, epochs),
                      "Iteration: {}".format(iteration),
                      "Avg. Training loss: {:.4f}".format(loss/100),
                      "{:.4f} sec/batch".format((end-start)/100))
                loss = 0
                start = time.time()
            
            if iteration % 1000 == 0:
                ## From Thushan Ganegedara's implementation
                # note that this is expensive (~20% slowdown if computed every 500 steps)
                sim = similarity.eval()
                for i in range(valid_size):
                    valid_word = int_to_vocab[valid_examples[i]]
                    top_k = 8 # number of nearest neighbors
                    nearest = (-sim[i, :]).argsort()[1:top_k+1]
                    log = 'Nearest to %s:' % valid_word
                    for k in range(top_k):
                        close_word = int_to_vocab[nearest[k]]
                        log = '%s %s,' % (log, close_word)
                    print(log)
            
            iteration += 1
    save_path = saver.save(sess, "checkpoints/text8.ckpt")
    embed_mat = sess.run(normalized_embedding)









    



Epoch 1/10 Iteration: 100 Avg. Training loss: 6.8987 0.1492 sec/batch
Epoch 1/10 Iteration: 200 Avg. Training loss: 6.5631 0.1521 sec/batch
Epoch 1/10 Iteration: 300 Avg. Training loss: 6.1568 0.1552 sec/batch
Epoch 1/10 Iteration: 400 Avg. Training loss: 5.7586 0.1704 sec/batch
Epoch 1/10 Iteration: 500 Avg. Training loss: 5.4138 0.1728 sec/batch
Epoch 1/10 Iteration: 600 Avg. Training loss: 5.1874 0.1730 sec/batch
Epoch 1/10 Iteration: 700 Avg. Training loss: 5.0200 0.1706 sec/batch
Epoch 1/10 Iteration: 800 Avg. Training loss: 4.8875 0.1709 sec/batch
Epoch 1/10 Iteration: 900 Avg. Training loss: 4.9282 0.1701 sec/batch
Epoch 1/10 Iteration: 1000 Avg. Training loss: 4.8595 0.1729 sec/batch
Nearest to however: growth, economic, scholars, infectious, send, fallen, workers, moves,
Nearest to no: epiphany, mosquito, chang, claims, ramsay, precursor, turn, discovery,
Nearest to were: classes, pennsylvania, olympics, solve, municipalities, february, intervention, cost,
Nearest to two: article, guarantees, mathematician, greco, nobility, metropolitan, c, unnatural,
Nearest to called: discussed, equations, governor, anxious, officer, depth, traditionally, flag,
Nearest to new: seek, casta, fr, connoisseur, kurt, crusades, resulted, faintly,
Nearest to as: trend, homo, writers, controversy, rare, sufficient, slow, mercurial,
Nearest to would: given, camille, likely, enough, history, turboprops, sons, performance,
Nearest to frac: vincent, races, resulted, nabokov, germany, zam, john, ath,
Nearest to defense: tradition, notably, handle, mainly, tuan, succeed, historical, gray,
Nearest to construction: here, revise, more, jud, padma, contains, pragmatic, terminal,
Nearest to engine: falling, actively, regents, gloucester, randell, offices, timeline, say,
Nearest to consists: strong, anchorage, swamp, treating, manasseh, boeing, postulating, religion,
Nearest to existence: overlap, shimura, justification, winnie, ois, leader, hardly, eliminated,
Nearest to troops: slogan, retaliation, homomorphism, from, successfully, trip, private, eva,
Nearest to egypt: postscript, left, boondocks, liberty, weber, retreat, homage, includes,
Epoch 1/10 Iteration: 1100 Avg. Training loss: 4.7720 0.1660 sec/batch
Epoch 1/10 Iteration: 1200 Avg. Training loss: 4.7780 0.1687 sec/batch
Epoch 1/10 Iteration: 1300 Avg. Training loss: 4.7014 0.1725 sec/batch
Epoch 1/10 Iteration: 1400 Avg. Training loss: 4.7105 0.1708 sec/batch
Epoch 1/10 Iteration: 1500 Avg. Training loss: 4.6734 0.1604 sec/batch
Epoch 1/10 Iteration: 1600 Avg. Training loss: 4.6827 0.1581 sec/batch
Epoch 1/10 Iteration: 1700 Avg. Training loss: 4.5865 0.1510 sec/batch
Epoch 1/10 Iteration: 1800 Avg. Training loss: 4.6501 0.1515 sec/batch
Epoch 1/10 Iteration: 1900 Avg. Training loss: 4.6213 0.1508 sec/batch
Epoch 1/10 Iteration: 2000 Avg. Training loss: 4.6087 0.1497 sec/batch
Nearest to however: growth, economic, scholars, dialects, moves, rs, ready, infectious,
Nearest to no: epiphany, precursor, mosquito, chang, ramsay, reflected, claims, gum,
Nearest to were: municipalities, classes, olympics, agi, firing, solve, pennsylvania, intervention,
Nearest to two: guarantees, greco, mathematician, metropolitan, genealogy, per, nc, nov,
Nearest to called: discussed, equations, spots, officer, bundestag, anxious, governor, nahuatl,
Nearest to new: seek, casta, kurt, merchandising, fr, connoisseur, faintly, crusades,
Nearest to as: trend, homo, sufficient, memorized, mercurial, anthem, taft, elite,
Nearest to would: tie, camille, insulate, uninhabited, turboprops, expository, enough, measurements,
Nearest to frac: races, vincent, resulted, nabokov, zam, john, ath, germany,
Nearest to defense: tradition, vultures, tuan, notably, handle, rahman, setlist, decomposed,
Nearest to construction: revise, jud, pragmatic, padma, elimination, here, isolated, bog,
Nearest to engine: falling, regents, actively, gloucester, randell, rachael, nobles, offices,
Nearest to consists: swamp, manasseh, anchorage, vaughan, glitter, postulating, treating, farming,
Nearest to existence: overlap, eliminated, shimura, winnie, irrational, justification, grabbing, minorities,
Nearest to troops: cardiac, slogan, homomorphism, trip, azores, retaliation, successfully, participating,
Nearest to egypt: postscript, left, promised, weber, boondocks, liberty, dsp, homage,
Epoch 1/10 Iteration: 2100 Avg. Training loss: 4.5705 0.1520 sec/batch
Epoch 1/10 Iteration: 2200 Avg. Training loss: 4.5153 0.1495 sec/batch
Epoch 1/10 Iteration: 2300 Avg. Training loss: 4.5204 0.1510 sec/batch
Epoch 1/10 Iteration: 2400 Avg. Training loss: 4.5606 0.1496 sec/batch
Epoch 1/10 Iteration: 2500 Avg. Training loss: 4.5173 0.1504 sec/batch
Epoch 1/10 Iteration: 2600 Avg. Training loss: 4.5042 0.1495 sec/batch
Epoch 1/10 Iteration: 2700 Avg. Training loss: 4.4662 0.1495 sec/batch
Epoch 1/10 Iteration: 2800 Avg. Training loss: 4.4462 0.1506 sec/batch
Epoch 1/10 Iteration: 2900 Avg. Training loss: 4.3585 0.1499 sec/batch
Epoch 1/10 Iteration: 3000 Avg. Training loss: 4.4921 0.1495 sec/batch
Nearest to however: economic, growth, participates, scholars, omit, rs, goiter, ready,
Nearest to no: epiphany, precursor, mosquito, slaughters, bursts, ramsay, gum, chang,
Nearest to were: classes, agi, municipalities, urals, olympics, skill, lasers, venture,
Nearest to two: guarantees, greco, guerra, per, genealogy, simulation, unnatural, metropolitan,
Nearest to called: discussed, bundestag, spots, crowning, equations, retinal, japh, monosaccharide,
Nearest to new: casta, kurt, seek, merchandising, faintly, connoisseur, crusades, fr,
Nearest to as: homo, trend, taft, mercurial, writers, secondary, shower, factual,
Nearest to would: tie, camille, insulate, cling, neighbour, turboprops, fluency, enough,
Nearest to frac: vincent, races, resulted, nabokov, provider, zam, ath, john,
Nearest to defense: tuan, vultures, setlist, tradition, decomposed, rahman, handle, newspeak,
Nearest to construction: revise, jud, padma, bog, pragmatic, elimination, curtain, eternity,
Nearest to engine: regents, falling, randell, gloucester, actively, rachael, nobles, origami,
Nearest to consists: manasseh, vaughan, glitter, proclaiming, bicentenary, chaff, anchorage, swamp,
Nearest to existence: overlap, shimura, eliminated, irrational, departmental, winnie, discharged, grabbing,
Nearest to troops: cardiac, homomorphism, slogan, azores, trip, pencil, knobs, successfully,
Nearest to egypt: postscript, left, promised, weber, boondocks, dsp, homage, cataphract,
Epoch 1/10 Iteration: 3100 Avg. Training loss: 4.4406 0.1535 sec/batch
Epoch 1/10 Iteration: 3200 Avg. Training loss: 4.4278 0.1495 sec/batch
Epoch 1/10 Iteration: 3300 Avg. Training loss: 4.4461 0.1514 sec/batch
Epoch 1/10 Iteration: 3400 Avg. Training loss: 4.4325 0.1493 sec/batch
Epoch 1/10 Iteration: 3500 Avg. Training loss: 4.3676 0.1494 sec/batch
Epoch 1/10 Iteration: 3600 Avg. Training loss: 4.4618 0.1496 sec/batch
Epoch 1/10 Iteration: 3700 Avg. Training loss: 4.4404 0.1518 sec/batch
Epoch 1/10 Iteration: 3800 Avg. Training loss: 4.3978 0.1497 sec/batch
Epoch 1/10 Iteration: 3900 Avg. Training loss: 4.4220 0.1499 sec/batch
Epoch 1/10 Iteration: 4000 Avg. Training loss: 4.3530 0.1499 sec/batch
Nearest to however: participates, goiter, omit, economic, ready, scholars, moves, rs,
Nearest to no: epiphany, precursor, mosquito, bursts, slaughters, gum, attenuated, nb,
Nearest to were: urals, agi, classes, municipalities, symphonic, skill, conquered, olympics,
Nearest to two: guarantees, greco, guerra, nov, genealogy, photius, simulation, laced,
Nearest to called: discussed, bundestag, retinal, crowning, robbery, spots, notre, kiswahili,
Nearest to new: casta, york, seek, fr, kurt, crusades, merchandising, connoisseur,
Nearest to as: homo, trend, taft, mercurial, such, factual, memorized, writers,
Nearest to would: tie, camille, cling, triadic, insulate, neighbour, fluency, desired,
Nearest to frac: vincent, zam, races, resulted, nabokov, provider, kj, right,
Nearest to defense: tuan, vultures, setlist, spire, farnham, decomposed, rahman, newspeak,
Nearest to construction: revise, jud, padma, pragmatic, elimination, bog, eternity, curtain,
Nearest to engine: regents, falling, randell, actively, rachael, origami, theobromine, gloucester,
Nearest to consists: vaughan, glitter, manasseh, proclaiming, renoir, noon, bicentenary, chaff,
Nearest to existence: overlap, shimura, eliminated, irrational, departmental, winnie, justification, discharged,
Nearest to troops: cardiac, homomorphism, slogan, azores, trip, knobs, successfully, sketchy,
Nearest to egypt: postscript, left, dsp, boondocks, cataphract, promised, weber, sars,
Epoch 1/10 Iteration: 4100 Avg. Training loss: 4.3949 0.1530 sec/batch
Epoch 1/10 Iteration: 4200 Avg. Training loss: 4.4199 0.1525 sec/batch
Epoch 1/10 Iteration: 4300 Avg. Training loss: 4.3641 0.1496 sec/batch
Epoch 1/10 Iteration: 4400 Avg. Training loss: 4.3997 0.1498 sec/batch
Epoch 1/10 Iteration: 4500 Avg. Training loss: 4.3330 0.1491 sec/batch
Epoch 1/10 Iteration: 4600 Avg. Training loss: 4.3884 0.1505 sec/batch
Epoch 1/10 Iteration: 4700 Avg. Training loss: 4.3862 0.1508 sec/batch
Epoch 1/10 Iteration: 4800 Avg. Training loss: 4.3888 0.1496 sec/batch
Epoch 1/10 Iteration: 4900 Avg. Training loss: 4.3865 0.1527 sec/batch
Epoch 1/10 Iteration: 5000 Avg. Training loss: 4.3474 0.1528 sec/batch
Nearest to however: participates, goiter, omit, rs, ready, moves, shogun, caso,
Nearest to no: epiphany, precursor, mosquito, longer, slaughters, washtenaw, bursts, gum,
Nearest to were: urals, agi, municipalities, beatmatching, symphonic, cracow, conquered, bases,
Nearest to two: guarantees, guerra, nov, nc, greco, photius, utterance, laced,
Nearest to called: bundestag, retinal, discussed, crowning, kiswahili, robbery, spots, japh,
Nearest to new: casta, york, seek, merchandising, maclean, connoisseur, kurt, patrolling,
Nearest to as: homo, such, taft, factual, trend, memorized, anthem, mercurial,
Nearest to would: tie, camille, triadic, cling, desired, fluency, neighbour, insulate,
Nearest to frac: right, vincent, n, zam, kj, omega, provider, nabokov,
Nearest to defense: tuan, vultures, setlist, spire, newspeak, redeemer, farnham, warship,
Nearest to construction: revise, jud, pragmatic, padma, elimination, gorey, bemba, berserker,
Nearest to engine: falling, regents, origami, randell, rachael, theobromine, actively, nobles,
Nearest to consists: vaughan, glitter, demiurge, manasseh, renoir, noon, chaff, bicentenary,
Nearest to existence: overlap, shimura, irrational, eliminated, justification, departmental, discharged, winnie,
Nearest to troops: cardiac, successfully, sketchy, azores, trip, distraction, knobs, slogan,
Nearest to egypt: postscript, dsp, left, boondocks, promised, cataphract, sars, cougars,
Epoch 1/10 Iteration: 5100 Avg. Training loss: 4.2837 0.1529 sec/batch
Epoch 1/10 Iteration: 5200 Avg. Training loss: 4.3531 0.1500 sec/batch
Epoch 1/10 Iteration: 5300 Avg. Training loss: 4.2931 0.1495 sec/batch
Epoch 1/10 Iteration: 5400 Avg. Training loss: 4.3509 0.1513 sec/batch
Epoch 1/10 Iteration: 5500 Avg. Training loss: 4.3330 0.1494 sec/batch
Epoch 1/10 Iteration: 5600 Avg. Training loss: 4.3325 0.1504 sec/batch
Epoch 1/10 Iteration: 5700 Avg. Training loss: 4.3506 0.1509 sec/batch
Epoch 1/10 Iteration: 5800 Avg. Training loss: 4.2887 0.1499 sec/batch
Epoch 1/10 Iteration: 5900 Avg. Training loss: 4.3436 0.1502 sec/batch
Epoch 1/10 Iteration: 6000 Avg. Training loss: 4.2820 0.1499 sec/batch
Nearest to however: goiter, participates, omit, rs, shogun, overdue, ready, rejection,
Nearest to no: epiphany, longer, precursor, washtenaw, gum, mosquito, slaughters, hypergeometric,
Nearest to were: urals, municipalities, agi, bases, beatmatching, symphonic, cracow, conquered,
Nearest to two: guerra, guarantees, nov, laced, paralleling, photius, utterance, nc,
Nearest to called: bundestag, retinal, kiswahili, spots, nominating, japh, robbery, infinitude,
Nearest to new: york, casta, seek, maclean, merchandising, patrolling, demeaning, connoisseur,
Nearest to as: such, homo, taft, mercurial, trend, factual, memorized, well,
Nearest to would: tie, triadic, fluency, camille, insulate, desired, peart, cling,
Nearest to frac: right, kj, vincent, zam, omega, n, provider, sin,
Nearest to defense: tuan, vultures, newspeak, setlist, spire, redeemer, farnham, decomposed,
Nearest to construction: revise, jud, padma, elimination, pragmatic, guesses, gorey, bemba,
Nearest to engine: falling, origami, regents, theobromine, rachael, randell, oversized, actively,
Nearest to consists: glitter, vaughan, manasseh, demiurge, chaff, noon, howie, bicentenary,
Nearest to existence: overlap, shimura, irrational, justification, eliminated, winnie, grabbing, departmental,
Nearest to troops: successfully, cardiac, knobs, war, azores, allies, distraction, commissioner,
Nearest to egypt: postscript, dsp, promised, cataphract, boondocks, poland, left, sars,
Epoch 1/10 Iteration: 6100 Avg. Training loss: 4.3025 0.1543 sec/batch
Epoch 1/10 Iteration: 6200 Avg. Training loss: 4.2568 0.1495 sec/batch
Epoch 1/10 Iteration: 6300 Avg. Training loss: 4.2945 0.1498 sec/batch
Epoch 1/10 Iteration: 6400 Avg. Training loss: 4.3246 0.1499 sec/batch
Epoch 1/10 Iteration: 6500 Avg. Training loss: 4.2791 0.1514 sec/batch
Epoch 1/10 Iteration: 6600 Avg. Training loss: 4.3164 0.1502 sec/batch
Epoch 1/10 Iteration: 6700 Avg. Training loss: 4.2305 0.1494 sec/batch
Epoch 1/10 Iteration: 6800 Avg. Training loss: 4.2714 0.1494 sec/batch
Epoch 1/10 Iteration: 6900 Avg. Training loss: 4.3083 0.1494 sec/batch
Epoch 1/10 Iteration: 7000 Avg. Training loss: 4.3061 0.1513 sec/batch
Nearest to however: goiter, participates, overdue, omit, ready, rejection, caso, plosive,
Nearest to no: longer, epiphany, precursor, slaughters, mosquito, hypergeometric, cytokine, gum,
Nearest to were: urals, agi, municipalities, beatmatching, cracow, symphonic, conquered, bases,
Nearest to two: guerra, guarantees, nc, nov, photius, spender, simulation, utterance,
Nearest to called: bundestag, retinal, crowning, actualization, animators, infinitude, nominating, kiswahili,
Nearest to new: york, casta, seek, maclean, merchandising, rename, patrolling, demeaning,
Nearest to as: such, taft, well, homo, mercurial, anthem, memorized, factual,
Nearest to would: triadic, tie, fluency, desired, peart, camille, cling, insulate,
Nearest to frac: right, omega, zam, vincent, kj, n, sin, provider,
Nearest to defense: tuan, vultures, newspeak, farnham, setlist, spire, warship, redeemer,
Nearest to construction: revise, jud, padma, pragmatic, guesses, elimination, gorey, bemba,
Nearest to engine: falling, origami, regents, theobromine, randell, rachael, reliability, oversized,
Nearest to consists: vaughan, glitter, demiurge, noon, bicentenary, chaff, treating, renoir,
Nearest to existence: overlap, irrational, shimura, justification, eliminated, grabbing, furnished, winnie,
Nearest to troops: successfully, war, cardiac, azores, allies, knobs, distraction, commissioner,
Nearest to egypt: postscript, dsp, promised, poland, boondocks, cataphract, sars, cougars,
Epoch 1/10 Iteration: 7100 Avg. Training loss: 4.3112 0.1530 sec/batch
Epoch 1/10 Iteration: 7200 Avg. Training loss: 4.2992 0.1502 sec/batch
Epoch 1/10 Iteration: 7300 Avg. Training loss: 4.2101 0.1500 sec/batch
Epoch 1/10 Iteration: 7400 Avg. Training loss: 4.3054 0.1497 sec/batch
Epoch 1/10 Iteration: 7500 Avg. Training loss: 4.2918 0.1498 sec/batch
Epoch 1/10 Iteration: 7600 Avg. Training loss: 4.2806 0.1508 sec/batch
Epoch 1/10 Iteration: 7700 Avg. Training loss: 4.3090 0.1498 sec/batch
Epoch 1/10 Iteration: 7800 Avg. Training loss: 4.2744 0.1492 sec/batch
Epoch 1/10 Iteration: 7900 Avg. Training loss: 4.2806 0.1498 sec/batch
Epoch 1/10 Iteration: 8000 Avg. Training loss: 4.2843 0.1516 sec/batch
Nearest to however: participates, goiter, overdue, ready, omit, moves, rejection, plosive,
Nearest to no: longer, epiphany, cytokine, mosquito, slaughters, washtenaw, precursor, hypergeometric,
Nearest to were: urals, agi, municipalities, conquered, cracow, beatmatching, symphonic, bases,
Nearest to two: guerra, guarantees, nc, nov, photius, simulation, spender, paralleling,
Nearest to called: bundestag, retinal, infinitude, actualization, crowning, animators, spots, notre,
Nearest to new: york, seek, casta, maclean, rename, patrolling, demeaning, disturbs,
Nearest to as: such, well, homo, taft, mercurial, unintelligent, memorized, anthem,
Nearest to would: triadic, fluency, desired, tie, peart, camille, insulate, likely,
Nearest to frac: right, omega, kj, n, zam, sin, vincent, parameter,
Nearest to defense: tuan, vultures, newspeak, farnham, spire, setlist, warship, trevelyan,
Nearest to construction: revise, jud, padma, pragmatic, guesses, gorey, elimination, bemba,
Nearest to engine: falling, origami, theobromine, regents, randell, oversized, toothpaste, rachael,
Nearest to consists: vaughan, glitter, bicentenary, demiurge, chaff, henceforward, noon, howie,
Nearest to existence: overlap, irrational, shimura, justification, grabbing, departmental, merle, eliminated,
Nearest to troops: successfully, war, allies, cardiac, commissioner, freed, participating, knobs,
Nearest to egypt: postscript, dsp, promised, boondocks, sars, poland, cataphract, cougars,
Epoch 1/10 Iteration: 8100 Avg. Training loss: 4.2696 0.1546 sec/batch
Epoch 1/10 Iteration: 8200 Avg. Training loss: 4.1661 0.1496 sec/batch
Epoch 1/10 Iteration: 8300 Avg. Training loss: 4.2118 0.1493 sec/batch
Epoch 1/10 Iteration: 8400 Avg. Training loss: 4.2254 0.1500 sec/batch
Epoch 1/10 Iteration: 8500 Avg. Training loss: 4.2434 0.1515 sec/batch
Epoch 1/10 Iteration: 8600 Avg. Training loss: 4.2109 0.1496 sec/batch
Epoch 1/10 Iteration: 8700 Avg. Training loss: 4.2828 0.1501 sec/batch
Epoch 1/10 Iteration: 8800 Avg. Training loss: 4.1164 0.1496 sec/batch
Epoch 1/10 Iteration: 8900 Avg. Training loss: 4.1401 0.1503 sec/batch
Epoch 1/10 Iteration: 9000 Avg. Training loss: 4.0940 0.1500 sec/batch
Nearest to however: goiter, participates, omit, ready, overdue, moves, plosive, shogun,
Nearest to no: longer, cytokine, epiphany, mosquito, washtenaw, hypergeometric, precursor, keck,
Nearest to were: urals, agi, conquered, municipalities, cracow, brasenose, symphonic, beatmatching,
Nearest to two: guerra, nov, nc, spender, marsden, mountaineer, untouchability, paralleling,
Nearest to called: bundestag, infinitude, retinal, nominating, crowning, animators, actualization, notre,
Nearest to new: york, zealand, casta, seek, rename, maclean, pluralism, patrolling,
Nearest to as: well, such, taft, homo, mercurial, anthem, ecumenical, memorized,
Nearest to would: triadic, fluency, seem, desired, tie, camille, peart, likely,
Nearest to frac: right, omega, zam, sin, kj, cdot, n, provider,
Nearest to defense: tuan, vultures, newspeak, farnham, spire, pannonia, staff, warship,
Nearest to construction: revise, jud, pragmatic, padma, guesses, elimination, curtain, terminal,
Nearest to engine: origami, falling, theobromine, toothpaste, regents, reliability, puckett, rachael,
Nearest to consists: glitter, vaughan, bicentenary, demiurge, henceforward, noon, chaff, grocers,
Nearest to existence: overlap, irrational, shimura, justification, merle, departmental, grabbing, winnie,
Nearest to troops: successfully, war, allies, cardiac, freed, participating, retaliation, distraction,
Nearest to egypt: postscript, dsp, promised, sars, boondocks, poland, cataphract, egyptian,
Epoch 1/10 Iteration: 9100 Avg. Training loss: 4.1857 0.1529 sec/batch
Epoch 1/10 Iteration: 9200 Avg. Training loss: 4.2511 0.1498 sec/batch
Epoch 1/10 Iteration: 9300 Avg. Training loss: 4.2612 0.1500 sec/batch
Epoch 1/10 Iteration: 9400 Avg. Training loss: 4.2332 0.1496 sec/batch
Epoch 1/10 Iteration: 9500 Avg. Training loss: 4.2995 0.1512 sec/batch
Epoch 1/10 Iteration: 9600 Avg. Training loss: 4.3005 0.1498 sec/batch
Epoch 1/10 Iteration: 9700 Avg. Training loss: 4.2747 0.1502 sec/batch
Epoch 1/10 Iteration: 9800 Avg. Training loss: 4.2386 0.1498 sec/batch
Epoch 1/10 Iteration: 9900 Avg. Training loss: 4.1062 0.1507 sec/batch
Epoch 1/10 Iteration: 10000 Avg. Training loss: 4.2487 0.1501 sec/batch
Nearest to however: goiter, participates, ready, overdue, moves, omit, shogun, taiko,
Nearest to no: longer, cytokine, epiphany, klang, washtenaw, mosquito, hypergeometric, keck,
Nearest to were: urals, agi, brasenose, municipalities, conquered, cracow, symphonic, bases,
Nearest to two: guerra, spender, nov, guarantees, photius, nc, ergodic, simulation,
Nearest to called: bundestag, infinitude, retinal, nominating, actualization, japh, kiswahili, animators,
Nearest to new: york, zealand, seek, rename, casta, disturbs, maclean, demeaning,
Nearest to as: such, well, memorized, homo, mercurial, taft, unintelligent, anthem,
Nearest to would: triadic, fluency, peart, desired, seem, likely, camille, tie,
Nearest to frac: right, omega, cdot, sin, kj, parameter, zam, n,
Nearest to defense: tuan, vultures, staff, farnham, newspeak, forces, pannonia, spire,
Nearest to construction: revise, jud, pragmatic, guesses, padma, curtain, elimination, terminal,
Nearest to engine: origami, falling, theobromine, toothpaste, puckett, reliability, regents, rachael,
Nearest to consists: glitter, vaughan, noon, bicentenary, henceforward, chaff, grocers, demiurge,
Nearest to existence: overlap, irrational, shimura, justification, merle, departmental, grabbing, enslave,
Nearest to troops: successfully, allies, retaliation, war, freed, participating, cardiac, azores,
Nearest to egypt: postscript, dsp, promised, boondocks, sars, poland, egyptian, cataphract,
Epoch 1/10 Iteration: 10100 Avg. Training loss: 4.2312 0.1528 sec/batch
Epoch 1/10 Iteration: 10200 Avg. Training loss: 4.2640 0.1510 sec/batch
Epoch 1/10 Iteration: 10300 Avg. Training loss: 4.2099 0.1498 sec/batch
Epoch 1/10 Iteration: 10400 Avg. Training loss: 4.2546 0.1508 sec/batch
Epoch 1/10 Iteration: 10500 Avg. Training loss: 4.2171 0.1501 sec/batch
Epoch 1/10 Iteration: 10600 Avg. Training loss: 4.1297 0.1515 sec/batch
Epoch 1/10 Iteration: 10700 Avg. Training loss: 4.0406 0.1500 sec/batch
Epoch 1/10 Iteration: 10800 Avg. Training loss: 4.2197 0.1501 sec/batch
Epoch 1/10 Iteration: 10900 Avg. Training loss: 4.1728 0.1501 sec/batch
Epoch 1/10 Iteration: 11000 Avg. Training loss: 4.1464 0.1497 sec/batch
Nearest to however: goiter, participates, overdue, moves, ready, omit, shogun, reviewers,
Nearest to no: longer, cytokine, epiphany, klang, mosquito, washtenaw, there, hypergeometric,
Nearest to were: urals, agi, brasenose, symphonic, cracow, conquered, municipalities, garibaldi,
Nearest to two: guerra, nov, spender, guarantees, photius, nc, mountaineer, untouchability,
Nearest to called: bundestag, infinitude, retinal, japh, kiswahili, actualization, nominating, investigates,
Nearest to new: york, zealand, rename, disturbs, seek, maclean, casta, dvorak,
Nearest to as: well, such, memorized, unintelligent, homo, mercurial, taft, referred,
Nearest to would: triadic, fluency, seem, peart, desired, likely, camille, flatten,
Nearest to frac: right, omega, cdot, sin, parameter, dt, zam, kj,
Nearest to defense: tuan, vultures, staff, farnham, pannonia, forces, ruy, spire,
Nearest to construction: revise, jud, elimination, pragmatic, curtain, terminal, guesses, padma,
Nearest to engine: origami, falling, toothpaste, theobromine, reliability, puckett, regents, avarice,
Nearest to consists: vaughan, noon, bicentenary, grocers, glitter, treating, henceforward, chaff,
Nearest to existence: overlap, irrational, shimura, justification, grabbing, merle, furnished, departmental,
Nearest to troops: successfully, allies, retaliation, war, freed, participating, distraction, azores,
Nearest to egypt: promised, postscript, dsp, egyptian, boondocks, sars, ancient, poland,
Epoch 1/10 Iteration: 11100 Avg. Training loss: 4.2112 0.1550 sec/batch
Epoch 1/10 Iteration: 11200 Avg. Training loss: 4.2200 0.1511 sec/batch
Epoch 1/10 Iteration: 11300 Avg. Training loss: 4.1936 0.1500 sec/batch
Epoch 1/10 Iteration: 11400 Avg. Training loss: 4.1729 0.1497 sec/batch
Epoch 1/10 Iteration: 11500 Avg. Training loss: 4.2071 0.1509 sec/batch
Epoch 1/10 Iteration: 11600 Avg. Training loss: 4.2511 0.1524 sec/batch
Epoch 2/10 Iteration: 11700 Avg. Training loss: 4.2259 0.0604 sec/batch
Epoch 2/10 Iteration: 11800 Avg. Training loss: 4.2064 0.1501 sec/batch
Epoch 2/10 Iteration: 11900 Avg. Training loss: 4.1479 0.1494 sec/batch
Epoch 2/10 Iteration: 12000 Avg. Training loss: 4.2002 0.1502 sec/batch
Nearest to however: goiter, overdue, participates, ready, moves, plosive, shogun, reviewers,
Nearest to no: longer, cytokine, epiphany, there, washtenaw, klang, peltier, hypergeometric,
Nearest to were: urals, brasenose, agi, cracow, symphonic, conquered, garibaldi, these,
Nearest to two: nov, guerra, photius, nc, guarantees, spender, ergodic, untouchability,
Nearest to called: bundestag, infinitude, retinal, japh, actualization, nominating, notre, investigates,
Nearest to new: york, zealand, rename, disturbs, seek, maclean, casta, pluralism,
Nearest to as: well, such, memorized, taft, homo, mercurial, unintelligent, referred,
Nearest to would: triadic, fluency, desired, seem, peart, likely, camille, cyclical,
Nearest to frac: right, cdot, omega, sin, dt, parameter, zam, kj,
Nearest to defense: tuan, vultures, staff, farnham, pannonia, forces, morecambe, newspeak,
Nearest to construction: revise, jud, elimination, pragmatic, curtain, padma, terminal, bemba,
Nearest to engine: origami, falling, petrol, toothpaste, combustion, bullet, reliability, regents,
Nearest to consists: vaughan, anchorage, glitter, noon, henceforward, chaff, grocers, dialectics,
Nearest to existence: irrational, overlap, shimura, justification, furnished, departmental, merle, exacting,
Nearest to troops: successfully, retaliation, freed, allies, distraction, war, azores, participating,
Nearest to egypt: ancient, egyptian, postscript, promised, dsp, sars, boondocks, poland,
Epoch 2/10 Iteration: 12100 Avg. Training loss: 4.1430 0.1542 sec/batch
Epoch 2/10 Iteration: 12200 Avg. Training loss: 4.1592 0.1495 sec/batch
Epoch 2/10 Iteration: 12300 Avg. Training loss: 4.1472 0.1504 sec/batch
Epoch 2/10 Iteration: 12400 Avg. Training loss: 4.1163 0.1500 sec/batch
Epoch 2/10 Iteration: 12500 Avg. Training loss: 4.1640 0.1504 sec/batch
Epoch 2/10 Iteration: 12600 Avg. Training loss: 4.1599 0.1495 sec/batch
Epoch 2/10 Iteration: 12700 Avg. Training loss: 4.1692 0.1499 sec/batch
Epoch 2/10 Iteration: 12800 Avg. Training loss: 4.1483 0.1520 sec/batch
Epoch 2/10 Iteration: 12900 Avg. Training loss: 4.1821 0.1524 sec/batch
Epoch 2/10 Iteration: 13000 Avg. Training loss: 4.1702 0.1507 sec/batch
Nearest to however: overdue, participates, goiter, ready, moves, sacco, omit, shogun,
Nearest to no: longer, cytokine, epiphany, there, washtenaw, keck, klang, mosquito,
Nearest to were: urals, brasenose, symphonic, conquered, cracow, these, municipalities, stolen,
Nearest to two: nc, guerra, nov, ergodic, untouchability, photius, spender, guarantees,
Nearest to called: bundestag, retinal, infinitude, sometimes, so, investigates, actualization, notre,
Nearest to new: york, zealand, rename, disturbs, seek, maclean, pluralism, casta,
Nearest to as: well, such, unintelligent, memorized, referred, taft, mercurial, homo,
Nearest to would: triadic, peart, fluency, seem, desired, flatten, camille, cyclical,
Nearest to frac: right, sin, cdot, omega, dt, zam, parameter, kj,
Nearest to defense: tuan, vultures, staff, farnham, pannonia, radicalization, newspeak, forces,
Nearest to construction: revise, jud, elimination, pragmatic, curtain, padma, terminal, tier,
Nearest to engine: origami, falling, petrol, regents, toothpaste, avarice, reliability, bullet,
Nearest to consists: vaughan, anchorage, henceforward, chaff, glitter, noon, grocers, bicentenary,
Nearest to existence: irrational, overlap, shimura, justification, exacting, departmental, lorry, grabbing,
Nearest to troops: retaliation, successfully, freed, azores, allies, distraction, participating, war,
Nearest to egypt: promised, ancient, egyptian, postscript, boondocks, sars, dsp, cataphract,
Epoch 2/10 Iteration: 13100 Avg. Training loss: 4.1678 0.1527 sec/batch
Epoch 2/10 Iteration: 13200 Avg. Training loss: 4.1473 0.1512 sec/batch
Epoch 2/10 Iteration: 13300 Avg. Training loss: 4.1745 0.1503 sec/batch
Epoch 2/10 Iteration: 13400 Avg. Training loss: 4.0416 0.1499 sec/batch
Epoch 2/10 Iteration: 13500 Avg. Training loss: 4.1818 0.1498 sec/batch
Epoch 2/10 Iteration: 13600 Avg. Training loss: 4.2059 0.1497 sec/batch
Epoch 2/10 Iteration: 13700 Avg. Training loss: 4.1632 0.1498 sec/batch
Epoch 2/10 Iteration: 13800 Avg. Training loss: 4.1632 0.1499 sec/batch
Epoch 2/10 Iteration: 13900 Avg. Training loss: 4.1714 0.1504 sec/batch
Epoch 2/10 Iteration: 14000 Avg. Training loss: 4.1767 0.1521 sec/batch
Nearest to however: goiter, overdue, participates, moves, ready, shogun, sacco, reviewers,
Nearest to no: longer, cytokine, epiphany, there, washtenaw, keck, klang, peltier,
Nearest to were: urals, brasenose, conquered, municipalities, cracow, stolen, symphonic, agi,
Nearest to two: nc, guerra, nov, ergodic, spender, photius, guarantees, untouchability,
Nearest to called: bundestag, retinal, infinitude, sometimes, notre, nominating, investigates, so,
Nearest to new: york, zealand, rename, disturbs, seek, maclean, pluralism, dvorak,
Nearest to as: well, such, taft, referred, memorized, unintelligent, mercurial, anthem,
Nearest to would: triadic, fluency, peart, desired, seem, flatten, camille, likely,
Nearest to frac: right, cdot, sin, omega, zam, dt, kj, parameter,
Nearest to defense: tuan, vultures, staff, pannonia, farnham, forces, radicalization, newspeak,
Nearest to construction: revise, jud, elimination, pragmatic, padma, curtain, terminal, cernunnos,
Nearest to engine: origami, petrol, falling, regents, toothpaste, bullet, avarice, reliability,
Nearest to consists: vaughan, henceforward, anchorage, glitter, shenanigans, grocers, nacional, chaff,
Nearest to existence: overlap, irrational, shimura, justification, lorry, exacting, departmental, merle,
Nearest to troops: retaliation, successfully, freed, azores, allies, distraction, participating, iraqi,
Nearest to egypt: promised, egyptian, ancient, postscript, boondocks, sars, dsp, cataphract,
Epoch 2/10 Iteration: 14100 Avg. Training loss: 4.1784 0.1526 sec/batch
Epoch 2/10 Iteration: 14200 Avg. Training loss: 4.1559 0.1495 sec/batch
Epoch 2/10 Iteration: 14300 Avg. Training loss: 4.1251 0.1499 sec/batch
Epoch 2/10 Iteration: 14400 Avg. Training loss: 4.1020 0.1525 sec/batch
Epoch 2/10 Iteration: 14500 Avg. Training loss: 4.0340 0.1498 sec/batch
Epoch 2/10 Iteration: 14600 Avg. Training loss: 4.0623 0.1497 sec/batch
Epoch 2/10 Iteration: 14700 Avg. Training loss: 4.1607 0.1501 sec/batch
Epoch 2/10 Iteration: 14800 Avg. Training loss: 4.0774 0.1500 sec/batch
Epoch 2/10 Iteration: 14900 Avg. Training loss: 4.1062 0.1503 sec/batch
Epoch 2/10 Iteration: 15000 Avg. Training loss: 4.1201 0.1499 sec/batch
Nearest to however: overdue, goiter, participates, moves, shogun, reviewers, ready, taiko,
Nearest to no: longer, there, cytokine, epiphany, keck, washtenaw, pulau, klang,
Nearest to were: urals, brasenose, municipalities, conquered, frankfurter, these, cracow, agi,
Nearest to two: spender, guerra, ergodic, nc, photius, guarantees, nov, untouchability,
Nearest to called: sometimes, infinitude, retinal, bundestag, so, investigates, notre, japh,
Nearest to new: york, zealand, rename, seek, disturbs, pluralism, maclean, papua,
Nearest to as: well, such, referred, taft, memorized, mercurial, anthem, unintelligent,
Nearest to would: triadic, fluency, desired, seem, peart, flatten, cyclical, ps,
Nearest to frac: right, cdot, sin, omega, dt, kj, zam, parameter,
Nearest to defense: tuan, staff, forces, farnham, pannonia, vultures, manpower, newspeak,
Nearest to construction: revise, elimination, terminal, pragmatic, curtain, jud, ilyich, cernunnos,
Nearest to engine: origami, petrol, toothpaste, falling, regents, guth, combustion, avarice,
Nearest to consists: nacional, henceforward, vaughan, anchorage, glitter, chaff, grocers, shenanigans,
Nearest to existence: overlap, irrational, shimura, justification, exacting, lorry, departmental, citymayors,
Nearest to troops: successfully, retaliation, freed, distraction, allies, azores, participating, iraqi,
Nearest to egypt: promised, egyptian, ancient, sars, dsp, boondocks, cataphract, postscript,
Epoch 2/10 Iteration: 15100 Avg. Training loss: 4.1278 0.1531 sec/batch
Epoch 2/10 Iteration: 15200 Avg. Training loss: 4.0670 0.1497 sec/batch
Epoch 2/10 Iteration: 15300 Avg. Training loss: 4.1311 0.1501 sec/batch
Epoch 2/10 Iteration: 15400 Avg. Training loss: 4.1478 0.1503 sec/batch
Epoch 2/10 Iteration: 15500 Avg. Training loss: 4.1363 0.1496 sec/batch
Epoch 2/10 Iteration: 15600 Avg. Training loss: 4.1429 0.1496 sec/batch
Epoch 2/10 Iteration: 15700 Avg. Training loss: 4.1273 0.1499 sec/batch
Epoch 2/10 Iteration: 15800 Avg. Training loss: 4.1599 0.1496 sec/batch
Epoch 2/10 Iteration: 15900 Avg. Training loss: 4.1511 0.1504 sec/batch
Epoch 2/10 Iteration: 16000 Avg. Training loss: 4.0788 0.1498 sec/batch
Nearest to however: overdue, participates, goiter, shogun, moves, reviewers, ready, taiko,
Nearest to no: longer, there, cytokine, keck, washtenaw, epiphany, klang, peltier,
Nearest to were: urals, brasenose, municipalities, frankfurter, these, stolen, conquered, symphonic,
Nearest to two: spender, guerra, nc, photius, nov, ergodic, untouchability, simulation,
Nearest to called: sometimes, retinal, so, bundestag, infinitude, investigates, notre, nominating,
Nearest to new: york, zealand, rename, seek, papua, pluralism, disturbs, maclean,
Nearest to as: well, such, taft, referred, memorized, unintelligent, anthem, mercurial,
Nearest to would: triadic, fluency, desired, seem, peart, cyclical, ps, dweezil,
Nearest to frac: right, cdot, omega, sin, dt, left, zam, sqrt,
Nearest to defense: staff, tuan, vultures, farnham, pannonia, forces, manpower, warship,
Nearest to construction: revise, elimination, pragmatic, curtain, jud, terminal, ilyich, tier,
Nearest to engine: origami, petrol, toothpaste, bullet, guth, combustion, falling, regents,
Nearest to consists: henceforward, vaughan, nacional, anchorage, glitter, chaff, shenanigans, grocers,
Nearest to existence: overlap, irrational, shimura, justification, exacting, lorry, departmental, enslave,
Nearest to troops: successfully, retaliation, freed, distraction, allies, azores, participating, sarin,
Nearest to egypt: egyptian, promised, ancient, cataphract, dsp, sars, boondocks, postscript,
Epoch 2/10 Iteration: 16100 Avg. Training loss: 4.1198 0.1525 sec/batch
Epoch 2/10 Iteration: 16200 Avg. Training loss: 4.1036 0.1498 sec/batch
Epoch 2/10 Iteration: 16300 Avg. Training loss: 4.1386 0.1500 sec/batch
Epoch 2/10 Iteration: 16400 Avg. Training loss: 4.1637 0.1498 sec/batch
Epoch 2/10 Iteration: 16500 Avg. Training loss: 4.1549 0.1496 sec/batch
Epoch 2/10 Iteration: 16600 Avg. Training loss: 4.1440 0.1514 sec/batch
Epoch 2/10 Iteration: 16700 Avg. Training loss: 4.0840 0.1504 sec/batch
Epoch 2/10 Iteration: 16800 Avg. Training loss: 4.0898 0.1502 sec/batch
Epoch 2/10 Iteration: 16900 Avg. Training loss: 4.0694 0.1502 sec/batch
Epoch 2/10 Iteration: 17000 Avg. Training loss: 4.1076 0.1515 sec/batch
Nearest to however: overdue, goiter, participates, moves, shogun, reviewers, pdpa, caucasians,
Nearest to no: longer, there, cytokine, keck, epiphany, washtenaw, pulau, klang,
Nearest to were: urals, brasenose, municipalities, these, frankfurter, cracow, conquered, symphonic,
Nearest to two: nc, nov, guerra, photius, spender, ergodic, untouchability, sheng,
Nearest to called: sometimes, so, infinitude, retinal, bundestag, investigates, monosaccharide, japh,
Nearest to new: york, zealand, rename, seek, papua, pluralism, casta, maclean,
Nearest to as: well, such, referred, taft, memorized, unintelligent, homo, mercurial,
Nearest to would: triadic, fluency, seem, desired, peart, flatten, cyclical, dweezil,
Nearest to frac: right, cdot, sin, omega, dt, mathrm, sqrt, kj,
Nearest to defense: staff, tuan, vultures, forces, pannonia, farnham, responsibility, manpower,
Nearest to construction: revise, elimination, curtain, pragmatic, jud, ilyich, earthen, tier,
Nearest to engine: petrol, origami, combustion, toothpaste, guth, bullet, regents, falling,
Nearest to consists: nacional, vaughan, henceforward, anchorage, glitter, grocers, gravesite, shenanigans,
Nearest to existence: overlap, irrational, shimura, exacting, justification, lorry, enslave, earthworm,
Nearest to troops: retaliation, freed, successfully, distraction, allies, azores, participating, sarin,
Nearest to egypt: egyptian, promised, sars, ancient, postscript, cataphract, boondocks, dsp,
Epoch 2/10 Iteration: 17100 Avg. Training loss: 4.1236 0.1531 sec/batch
Epoch 2/10 Iteration: 17200 Avg. Training loss: 4.1196 0.1498 sec/batch
Epoch 2/10 Iteration: 17300 Avg. Training loss: 4.1679 0.1498 sec/batch
Epoch 2/10 Iteration: 17400 Avg. Training loss: 4.1289 0.1496 sec/batch
Epoch 2/10 Iteration: 17500 Avg. Training loss: 4.0641 0.1504 sec/batch
Epoch 2/10 Iteration: 17600 Avg. Training loss: 4.1227 0.1499 sec/batch
Epoch 2/10 Iteration: 17700 Avg. Training loss: 4.0889 0.1500 sec/batch
Epoch 2/10 Iteration: 17800 Avg. Training loss: 4.1420 0.1503 sec/batch
Epoch 2/10 Iteration: 17900 Avg. Training loss: 3.9900 0.1512 sec/batch
Epoch 2/10 Iteration: 18000 Avg. Training loss: 4.1323 0.1498 sec/batch
Nearest to however: overdue, participates, goiter, reviewers, shogun, pdpa, moves, omit,
Nearest to no: longer, there, cytokine, keck, epiphany, pulau, washtenaw, klang,
Nearest to were: urals, brasenose, these, municipalities, symphonic, cracow, frankfurter, stolen,
Nearest to two: guerra, spender, nov, ergodic, nc, untouchability, sheng, photius,
Nearest to called: sometimes, so, bundestag, infinitude, investigates, retinal, monosaccharide, japh,
Nearest to new: york, zealand, rename, seek, papua, pluralism, casta, disturbs,
Nearest to as: well, such, taft, referred, memorized, unintelligent, known, mercurial,
Nearest to would: triadic, fluency, seem, desired, peart, dweezil, cyclical, likely,
Nearest to frac: right, cdot, omega, sin, dt, mathrm, sqrt, cdots,
Nearest to defense: tuan, staff, vultures, farnham, responsibility, pannonia, morecambe, warship,
Nearest to construction: revise, elimination, pragmatic, curtain, jud, ilyich, frobisher, tier,
Nearest to engine: petrol, origami, combustion, modulating, toothpaste, bullet, guth, regents,
Nearest to consists: nacional, vaughan, henceforward, glitter, anchorage, dialectics, tachelhit, bottle,
Nearest to existence: irrational, overlap, shimura, justification, exacting, lorry, enslave, departmental,
Nearest to troops: retaliation, successfully, freed, allies, azores, distraction, participating, sarin,
Nearest to egypt: egyptian, promised, ancient, sars, cataphract, boondocks, postscript, dsp,
Epoch 2/10 Iteration: 18100 Avg. Training loss: 4.1364 0.1528 sec/batch
Epoch 2/10 Iteration: 18200 Avg. Training loss: 4.0755 0.1497 sec/batch
Epoch 2/10 Iteration: 18300 Avg. Training loss: 4.0201 0.1509 sec/batch
Epoch 2/10 Iteration: 18400 Avg. Training loss: 4.1344 0.1506 sec/batch
Epoch 2/10 Iteration: 18500 Avg. Training loss: 4.1362 0.1499 sec/batch
Epoch 2/10 Iteration: 18600 Avg. Training loss: 4.1196 0.1498 sec/batch
Epoch 2/10 Iteration: 18700 Avg. Training loss: 4.1237 0.1494 sec/batch
Epoch 2/10 Iteration: 18800 Avg. Training loss: 4.1756 0.1515 sec/batch
Epoch 2/10 Iteration: 18900 Avg. Training loss: 4.1236 0.1499 sec/batch
Epoch 2/10 Iteration: 19000 Avg. Training loss: 4.0718 0.1494 sec/batch
Nearest to however: overdue, participates, goiter, moves, pdpa, reviewers, caucasians, shogun,
Nearest to no: longer, there, keck, cytokine, epiphany, pulau, washtenaw, klang,
Nearest to were: urals, brasenose, these, municipalities, conquered, frankfurter, agi, symphonic,
Nearest to two: spender, nc, ergodic, nov, photius, guerra, untouchability, guarantees,
Nearest to called: so, sometimes, infinitude, retinal, bundestag, investigates, notre, galerius,
Nearest to new: york, zealand, papua, rename, seek, pluralism, casta, disturbs,
Nearest to as: well, such, referred, taft, memorized, known, mercurial, anthem,
Nearest to would: triadic, fluency, peart, seem, dweezil, cyclical, desired, likely,
Nearest to frac: right, cdot, sin, omega, dt, mathrm, sqrt, cdots,
Nearest to defense: staff, vultures, tuan, pannonia, farnham, morecambe, responsibility, forces,
Nearest to construction: revise, elimination, pragmatic, curtain, ilyich, jud, earthen, frobisher,
Nearest to engine: petrol, origami, combustion, bullet, regents, guth, modulating, avarice,
Nearest to consists: nacional, vaughan, henceforward, anchorage, dialectics, glitter, tachelhit, bottle,
Nearest to existence: overlap, irrational, shimura, lorry, exacting, justification, enslave, departmental,
Nearest to troops: successfully, retaliation, freed, allies, azores, distraction, participating, sarin,
Nearest to egypt: ancient, egyptian, promised, sars, cataphract, boondocks, dsp, postscript,
Epoch 2/10 Iteration: 19100 Avg. Training loss: 4.1586 0.1524 sec/batch
Epoch 2/10 Iteration: 19200 Avg. Training loss: 4.1281 0.1504 sec/batch
Epoch 2/10 Iteration: 19300 Avg. Training loss: 4.1159 0.1507 sec/batch
Epoch 2/10 Iteration: 19400 Avg. Training loss: 4.1196 0.1497 sec/batch
Epoch 2/10 Iteration: 19500 Avg. Training loss: 4.1414 0.1499 sec/batch
Epoch 2/10 Iteration: 19600 Avg. Training loss: 4.1298 0.1500 sec/batch
Epoch 2/10 Iteration: 19700 Avg. Training loss: 4.1312 0.1509 sec/batch
Epoch 2/10 Iteration: 19800 Avg. Training loss: 4.0346 0.1495 sec/batch
Epoch 2/10 Iteration: 19900 Avg. Training loss: 4.0816 0.1511 sec/batch
Epoch 2/10 Iteration: 20000 Avg. Training loss: 4.0734 0.1502 sec/batch
Nearest to however: overdue, participates, goiter, moves, reviewers, pdpa, plosive, caso,
Nearest to no: longer, there, keck, cytokine, washtenaw, epiphany, pulau, klang,
Nearest to were: urals, brasenose, these, municipalities, frankfurter, agi, conquered, symphonic,
Nearest to two: nc, spender, guarantees, photius, nov, ergodic, guerra, untouchability,
Nearest to called: so, sometimes, retinal, infinitude, bundestag, investigates, flaccus, notre,
Nearest to new: york, zealand, papua, seek, rename, pluralism, disturbs, suffect,
Nearest to as: well, such, referred, memorized, taft, known, unintelligent, mercurial,
Nearest to would: triadic, fluency, seem, peart, desired, cyclical, dweezil, flatten,
Nearest to frac: right, cdot, sin, dt, omega, mathrm, sqrt, zam,
Nearest to defense: vultures, staff, tuan, farnham, pannonia, morecambe, warship, pmid,
Nearest to construction: revise, elimination, pragmatic, ilyich, curtain, materials, jud, tier,
Nearest to engine: petrol, combustion, origami, engines, bullet, modulating, toothpaste, avarice,
Nearest to consists: vaughan, nacional, henceforward, anchorage, dialectics, glitter, tachelhit, arne,
Nearest to existence: irrational, overlap, shimura, exacting, lorry, enslave, justification, departmental,
Nearest to troops: retaliation, freed, successfully, allies, azores, distraction, iraqi, participating,
Nearest to egypt: egyptian, ancient, promised, sars, cataphract, boondocks, dsp, homage,
Epoch 2/10 Iteration: 20100 Avg. Training loss: 4.0858 0.1549 sec/batch
Epoch 2/10 Iteration: 20200 Avg. Training loss: 4.0914 0.1500 sec/batch
Epoch 2/10 Iteration: 20300 Avg. Training loss: 4.0432 0.1496 sec/batch
Epoch 2/10 Iteration: 20400 Avg. Training loss: 4.1204 0.1507 sec/batch
Epoch 2/10 Iteration: 20500 Avg. Training loss: 3.8504 0.1494 sec/batch
Epoch 2/10 Iteration: 20600 Avg. Training loss: 4.0499 0.1494 sec/batch
Epoch 2/10 Iteration: 20700 Avg. Training loss: 3.9740 0.1501 sec/batch
Epoch 2/10 Iteration: 20800 Avg. Training loss: 4.1116 0.1523 sec/batch
Epoch 2/10 Iteration: 20900 Avg. Training loss: 4.1208 0.1497 sec/batch
Epoch 2/10 Iteration: 21000 Avg. Training loss: 4.1162 0.1492 sec/batch
Nearest to however: overdue, participates, goiter, pdpa, shogun, moves, reviewers, asylum,
Nearest to no: longer, there, keck, cytokine, washtenaw, pulau, epiphany, isonzo,
Nearest to were: urals, brasenose, these, symphonic, stolen, municipalities, cracow, printed,
Nearest to two: nov, guerra, nc, spender, untouchability, ergodic, photius, mountaineer,
Nearest to called: so, infinitude, sometimes, investigates, bundestag, retinal, notre, flaccus,
Nearest to new: york, zealand, papua, seek, rename, pluralism, ny, disturbs,
Nearest to as: well, such, taft, referred, known, mercurial, memorized, unintelligent,
Nearest to would: triadic, fluency, seem, peart, dweezil, desired, likely, berlusconi,
Nearest to frac: right, cdot, dt, sin, omega, mathrm, sqrt, cdots,
Nearest to defense: vultures, staff, tuan, farnham, morecambe, pannonia, pmid, responsibility,
Nearest to construction: revise, elimination, pragmatic, curtain, ilyich, frobisher, bemba, tier,
Nearest to engine: petrol, combustion, origami, engines, toothpaste, modulating, bullet, avarice,
Nearest to consists: vaughan, anchorage, henceforward, nacional, dialectics, arne, glitter, tachelhit,
Nearest to existence: irrational, overlap, lorry, exacting, shimura, enslave, justification, departmental,
Nearest to troops: retaliation, freed, successfully, azores, allies, distraction, participating, iraqi,
Nearest to egypt: egyptian, ancient, promised, sars, dsp, cataphract, postscript, boondocks,
Epoch 2/10 Iteration: 21100 Avg. Training loss: 4.1676 0.1527 sec/batch
Epoch 2/10 Iteration: 21200 Avg. Training loss: 4.1668 0.1496 sec/batch
Epoch 2/10 Iteration: 21300 Avg. Training loss: 4.1729 0.1496 sec/batch
Epoch 2/10 Iteration: 21400 Avg. Training loss: 4.1342 0.1505 sec/batch
Epoch 2/10 Iteration: 21500 Avg. Training loss: 4.0403 0.1496 sec/batch
Epoch 2/10 Iteration: 21600 Avg. Training loss: 4.0345 0.1498 sec/batch
Epoch 2/10 Iteration: 21700 Avg. Training loss: 4.1101 0.1496 sec/batch
Epoch 2/10 Iteration: 21800 Avg. Training loss: 4.0586 0.1512 sec/batch
Epoch 2/10 Iteration: 21900 Avg. Training loss: 4.1055 0.1513 sec/batch
Epoch 2/10 Iteration: 22000 Avg. Training loss: 4.1227 0.1496 sec/batch
Nearest to however: overdue, participates, shogun, moves, goiter, pdpa, reviewers, sacco,
Nearest to no: longer, there, keck, cytokine, pulau, washtenaw, epiphany, peltier,
Nearest to were: urals, brasenose, these, symphonic, stolen, municipalities, agi, benchers,
Nearest to two: ergodic, spender, nc, nov, guerra, photius, guarantees, folic,
Nearest to called: so, infinitude, sometimes, investigates, retinal, bundestag, japh, emptying,
Nearest to new: york, zealand, papua, rename, seek, disturbs, ny, pluralism,
Nearest to as: well, such, referred, taft, known, memorized, mercurial, unintelligent,
Nearest to would: fluency, triadic, seem, peart, desired, dweezil, flatten, cyclical,
Nearest to frac: right, cdot, sin, dt, omega, mathrm, sqrt, cdots,
Nearest to defense: vultures, staff, tuan, farnham, morecambe, pannonia, responsibility, warship,
Nearest to construction: revise, elimination, pragmatic, ilyich, materials, curtain, measure, frobisher,
Nearest to engine: petrol, combustion, origami, engines, toothpaste, modulating, avarice, guth,
Nearest to consists: vaughan, nacional, arne, magistrate, anchorage, bottle, henceforward, tachelhit,
Nearest to existence: irrational, overlap, exacting, lorry, shimura, enslave, justification, citymayors,
Nearest to troops: retaliation, freed, azores, successfully, allies, distraction, participating, justified,
Nearest to egypt: egyptian, ancient, promised, sars, cataphract, boondocks, dsp, postscript,
Epoch 2/10 Iteration: 22100 Avg. Training loss: 4.0937 0.1526 sec/batch
Epoch 2/10 Iteration: 22200 Avg. Training loss: 4.1073 0.1490 sec/batch
Epoch 2/10 Iteration: 22300 Avg. Training loss: 3.9351 0.1495 sec/batch
Epoch 2/10 Iteration: 22400 Avg. Training loss: 3.9716 0.1499 sec/batch
Epoch 2/10 Iteration: 22500 Avg. Training loss: 4.1027 0.1491 sec/batch
Epoch 2/10 Iteration: 22600 Avg. Training loss: 4.0065 0.1493 sec/batch
Epoch 2/10 Iteration: 22700 Avg. Training loss: 4.0829 0.1493 sec/batch
Epoch 2/10 Iteration: 22800 Avg. Training loss: 4.1105 0.1497 sec/batch
Epoch 2/10 Iteration: 22900 Avg. Training loss: 4.0892 0.1492 sec/batch
Epoch 2/10 Iteration: 23000 Avg. Training loss: 4.0516 0.1493 sec/batch
Nearest to however: overdue, participates, reviewers, pdpa, shogun, moves, goiter, sacco,
Nearest to no: longer, there, cytokine, keck, washtenaw, pulau, isonzo, epiphany,
Nearest to were: brasenose, these, urals, symphonic, printed, municipalities, stolen, benchers,
Nearest to two: nc, nov, spender, ergodic, guerra, equ, guarantees, photius,
Nearest to called: so, infinitude, sometimes, investigates, retinal, bundestag, japh, galerius,
Nearest to new: york, zealand, papua, rename, seek, disturbs, ny, suffect,
Nearest to as: well, such, referred, known, taft, memorized, unintelligent, mercurial,
Nearest to would: triadic, fluency, peart, seem, flatten, dweezil, desired, questioner,
Nearest to frac: right, cdot, sin, omega, dt, mathrm, sqrt, cdots,
Nearest to defense: vultures, tuan, staff, farnham, morecambe, pannonia, responsibility, forces,
Nearest to construction: elimination, revise, pragmatic, materials, ilyich, curtain, frobisher, badged,
Nearest to engine: petrol, combustion, origami, engines, avarice, cordobas, toothpaste, guth,
Nearest to consists: vaughan, nacional, anchorage, magistrate, tachelhit, henceforward, arne, bottle,
Nearest to existence: overlap, irrational, lorry, exacting, shimura, enslave, citymayors, justification,
Nearest to troops: retaliation, freed, azores, successfully, allies, distraction, justified, participating,
Nearest to egypt: egyptian, ancient, promised, boondocks, sars, cataphract, postscript, homage,
Epoch 2/10 Iteration: 23100 Avg. Training loss: 4.1187 0.1522 sec/batch
Epoch 2/10 Iteration: 23200 Avg. Training loss: 4.0688 0.1500 sec/batch
Epoch 2/10 Iteration: 23300 Avg. Training loss: 4.1706 0.1494 sec/batch
Epoch 3/10 Iteration: 23400 Avg. Training loss: 4.1238 0.1199 sec/batch
Epoch 3/10 Iteration: 23500 Avg. Training loss: 4.0381 0.1496 sec/batch
Epoch 3/10 Iteration: 23600 Avg. Training loss: 4.0899 0.1496 sec/batch
Epoch 3/10 Iteration: 23700 Avg. Training loss: 4.0668 0.1498 sec/batch
Epoch 3/10 Iteration: 23800 Avg. Training loss: 4.0693 0.1495 sec/batch
Epoch 3/10 Iteration: 23900 Avg. Training loss: 4.0853 0.1498 sec/batch
Epoch 3/10 Iteration: 24000 Avg. Training loss: 4.0530 0.1497 sec/batch
Nearest to however: overdue, participates, pdpa, sacco, reviewers, goiter, moves, shogun,
Nearest to no: longer, there, keck, cytokine, washtenaw, pulau, epiphany, eelam,
Nearest to were: brasenose, urals, these, printed, they, stolen, benchers, symphonic,
Nearest to two: nov, guerra, ergodic, nc, spender, wrestlemania, equ, sheng,
Nearest to called: so, sometimes, infinitude, investigates, retinal, bundestag, galerius, flaccus,
Nearest to new: york, zealand, papua, rename, seek, ny, pluralism, disturbs,
Nearest to as: well, such, referred, known, taft, mercurial, unintelligent, memorized,
Nearest to would: triadic, fluency, seem, peart, flatten, cyclical, desired, dweezil,
Nearest to frac: right, cdot, sin, omega, dt, mathrm, sqrt, cdots,
Nearest to defense: vultures, tuan, staff, responsibility, morecambe, farnham, pannonia, forces,
Nearest to construction: elimination, revise, ilyich, materials, pragmatic, curtain, cut, frobisher,
Nearest to engine: petrol, combustion, engines, origami, avarice, cordobas, automobile, toothpaste,
Nearest to consists: vaughan, nacional, anchorage, magistrate, arne, dialectics, tachelhit, chaff,
Nearest to existence: irrational, overlap, exacting, lorry, shimura, enslave, departmental, kinetochores,
Nearest to troops: retaliation, freed, azores, successfully, allies, sarin, distraction, justified,
Nearest to egypt: egyptian, ancient, promised, boondocks, sars, postscript, cataphract, dsp,
Epoch 3/10 Iteration: 24100 Avg. Training loss: 4.0197 0.1522 sec/batch
Epoch 3/10 Iteration: 24200 Avg. Training loss: 4.0766 0.1490 sec/batch
Epoch 3/10 Iteration: 24300 Avg. Training loss: 4.0709 0.1499 sec/batch
Epoch 3/10 Iteration: 24400 Avg. Training loss: 4.0609 0.1494 sec/batch
Epoch 3/10 Iteration: 24500 Avg. Training loss: 4.0756 0.1496 sec/batch
Epoch 3/10 Iteration: 24600 Avg. Training loss: 4.1081 0.1495 sec/batch
Epoch 3/10 Iteration: 24700 Avg. Training loss: 4.0845 0.1497 sec/batch
Epoch 3/10 Iteration: 24800 Avg. Training loss: 4.0684 0.1499 sec/batch
Epoch 3/10 Iteration: 24900 Avg. Training loss: 4.0684 0.1494 sec/batch
Epoch 3/10 Iteration: 25000 Avg. Training loss: 3.9587 0.1497 sec/batch
Nearest to however: overdue, participates, pdpa, goiter, sacco, taiko, reviewers, shogun,
Nearest to no: longer, there, cytokine, keck, washtenaw, pulau, eelam, epiphany,
Nearest to were: brasenose, urals, stolen, printed, these, municipalities, they, benchers,
Nearest to two: nov, ergodic, nc, spender, guerra, guarantees, equ, sheng,
Nearest to called: so, sometimes, infinitude, investigates, retinal, bundestag, galerius, flaccus,
Nearest to new: york, zealand, papua, seek, rename, pluralism, ny, disturbs,
Nearest to as: well, such, referred, known, taft, result, unintelligent, mercurial,
Nearest to would: triadic, fluency, peart, seem, desired, dweezil, questioner, flatten,
Nearest to frac: right, cdot, dt, sin, omega, mathrm, sqrt, cdots,
Nearest to defense: vultures, tuan, staff, responsibility, forces, pannonia, farnham, manpower,
Nearest to construction: elimination, revise, materials, pragmatic, ilyich, cut, tier, frobisher,
Nearest to engine: petrol, combustion, engines, origami, chassis, avarice, toothpaste, steering,
Nearest to consists: nacional, vaughan, anchorage, magistrate, tachelhit, dialectics, henceforward, arne,
Nearest to existence: overlap, irrational, exacting, lorry, shimura, enslave, kinetochores, departmental,
Nearest to troops: retaliation, freed, azores, successfully, distraction, sarin, allies, justified,
Nearest to egypt: egyptian, promised, ancient, cataphract, boondocks, sars, postscript, bloodthirsty,
Epoch 3/10 Iteration: 25100 Avg. Training loss: 4.0958 0.1526 sec/batch
Epoch 3/10 Iteration: 25200 Avg. Training loss: 4.0697 0.1496 sec/batch
Epoch 3/10 Iteration: 25300 Avg. Training loss: 4.1126 0.1496 sec/batch
Epoch 3/10 Iteration: 25400 Avg. Training loss: 4.0853 0.1497 sec/batch
Epoch 3/10 Iteration: 25500 Avg. Training loss: 4.0915 0.1495 sec/batch
Epoch 3/10 Iteration: 25600 Avg. Training loss: 4.0786 0.1502 sec/batch
Epoch 3/10 Iteration: 25700 Avg. Training loss: 4.1199 0.1495 sec/batch
Epoch 3/10 Iteration: 25800 Avg. Training loss: 4.0677 0.1496 sec/batch
Epoch 3/10 Iteration: 25900 Avg. Training loss: 4.1035 0.1495 sec/batch
Epoch 3/10 Iteration: 26000 Avg. Training loss: 4.0134 0.1496 sec/batch
Nearest to however: overdue, participates, pdpa, goiter, moves, reviewers, taiko, sacco,
Nearest to no: longer, there, cytokine, keck, washtenaw, peltier, eelam, isonzo,
Nearest to were: brasenose, these, urals, stolen, printed, they, municipalities, sensational,
Nearest to two: nov, ergodic, spender, nc, guarantees, guerra, equ, untouchability,
Nearest to called: so, sometimes, investigates, infinitude, retinal, galerius, bundestag, notre,
Nearest to new: york, zealand, papua, rename, seek, pluralism, disturbs, ny,
Nearest to as: well, such, referred, known, taft, result, unintelligent, memorized,
Nearest to would: triadic, fluency, flatten, seem, peart, be, desired, questioner,
Nearest to frac: right, cdot, dt, omega, sin, mathrm, sqrt, cdots,
Nearest to defense: vultures, tuan, forces, staff, responsibility, pannonia, farnham, morecambe,
Nearest to construction: elimination, revise, pragmatic, materials, ilyich, curtain, frobisher, documenting,
Nearest to engine: petrol, combustion, engines, origami, regents, chassis, bullet, cordobas,
Nearest to consists: nacional, vaughan, anchorage, magistrate, dialectics, henceforward, arne, tachelhit,
Nearest to existence: irrational, exacting, lorry, overlap, shimura, enslave, baptise, tilapia,
Nearest to troops: retaliation, freed, successfully, azores, justified, distraction, allies, sarin,
Nearest to egypt: egyptian, ancient, promised, cataphract, boondocks, sars, bloodthirsty, homage,
Epoch 3/10 Iteration: 26100 Avg. Training loss: 4.0163 0.1524 sec/batch
Epoch 3/10 Iteration: 26200 Avg. Training loss: 3.8949 0.1496 sec/batch
Epoch 3/10 Iteration: 26300 Avg. Training loss: 4.0531 0.1493 sec/batch
Epoch 3/10 Iteration: 26400 Avg. Training loss: 4.0263 0.1500 sec/batch
Epoch 3/10 Iteration: 26500 Avg. Training loss: 4.0539 0.1495 sec/batch
Epoch 3/10 Iteration: 26600 Avg. Training loss: 4.0310 0.1496 sec/batch
Epoch 3/10 Iteration: 26700 Avg. Training loss: 4.0319 0.1497 sec/batch
Epoch 3/10 Iteration: 26800 Avg. Training loss: 3.9308 0.1496 sec/batch
Epoch 3/10 Iteration: 26900 Avg. Training loss: 4.0608 0.1494 sec/batch
Epoch 3/10 Iteration: 27000 Avg. Training loss: 4.0885 0.1497 sec/batch
Nearest to however: overdue, participates, moves, pdpa, shogun, goiter, reviewers, taiko,
Nearest to no: longer, there, cytokine, keck, washtenaw, peltier, oo, cheapest,
Nearest to were: brasenose, printed, stolen, they, these, urals, municipalities, symphonic,
Nearest to two: nov, ergodic, spender, nc, sheng, guerra, equ, folic,
Nearest to called: so, sometimes, investigates, infinitude, retinal, galerius, bundestag, emptying,
Nearest to new: york, zealand, papua, rename, caledonia, seek, pluralism, ny,
Nearest to as: well, such, referred, known, taft, result, regarded, mercurial,
Nearest to would: triadic, fluency, flatten, peart, seem, be, desired, dweezil,
Nearest to frac: right, cdot, sin, dt, omega, mathrm, sqrt, mbox,
Nearest to defense: vultures, tuan, staff, forces, responsibility, farnham, morecambe, kune,
Nearest to construction: elimination, ilyich, pragmatic, revise, materials, frobisher, curtain, cut,
Nearest to engine: petrol, combustion, engines, origami, regents, chassis, avarice, bullet,
Nearest to consists: nacional, vaughan, anchorage, magistrate, spaces, henceforward, dialectics, arne,
Nearest to existence: exacting, irrational, lorry, overlap, shimura, enslave, reinforcing, baptise,
Nearest to troops: retaliation, freed, successfully, azores, sarin, distraction, allies, justified,
Nearest to egypt: egyptian, promised, ancient, boondocks, cataphract, sars, homage, bloodthirsty,
Epoch 3/10 Iteration: 27100 Avg. Training loss: 4.0800 0.1523 sec/batch
Epoch 3/10 Iteration: 27200 Avg. Training loss: 4.0601 0.1504 sec/batch
Epoch 3/10 Iteration: 27300 Avg. Training loss: 4.0605 0.1501 sec/batch
Epoch 3/10 Iteration: 27400 Avg. Training loss: 4.0584 0.1497 sec/batch
Epoch 3/10 Iteration: 27500 Avg. Training loss: 4.1066 0.1492 sec/batch
Epoch 3/10 Iteration: 27600 Avg. Training loss: 4.0128 0.1504 sec/batch
Epoch 3/10 Iteration: 27700 Avg. Training loss: 4.0604 0.1500 sec/batch
Epoch 3/10 Iteration: 27800 Avg. Training loss: 4.0179 0.1496 sec/batch
Epoch 3/10 Iteration: 27900 Avg. Training loss: 4.0831 0.1502 sec/batch
Epoch 3/10 Iteration: 28000 Avg. Training loss: 4.0741 0.1494 sec/batch
Nearest to however: overdue, participates, reviewers, moves, sacco, shogun, pdpa, goiter,
Nearest to no: longer, there, cytokine, keck, washtenaw, oo, isonzo, peltier,
Nearest to were: brasenose, printed, they, stolen, symphonic, these, sensational, urals,
Nearest to two: nov, ergodic, spender, nc, sheng, folic, guarantees, equ,
Nearest to called: so, sometimes, investigates, infinitude, retinal, galerius, bundestag, flaccus,
Nearest to new: york, zealand, papua, rename, seek, caledonia, pluralism, suffect,
Nearest to as: well, such, referred, known, taft, result, regarded, unintelligent,
Nearest to would: triadic, fluency, seem, peart, dweezil, flatten, desired, be,
Nearest to frac: right, cdot, sin, dt, omega, left, mathrm, zam,
Nearest to defense: vultures, tuan, staff, morecambe, kune, responsibility, farnham, warship,
Nearest to construction: elimination, ilyich, pragmatic, materials, revise, curtain, frobisher, cut,
Nearest to engine: petrol, combustion, engines, origami, regents, chassis, bullet, sevens,
Nearest to consists: nacional, vaughan, magistrate, anchorage, dialectics, henceforward, arne, spaces,
Nearest to existence: irrational, exacting, lorry, overlap, shimura, enslave, reinforcing, izanami,
Nearest to troops: retaliation, freed, successfully, azores, sarin, allies, sanatorium, justified,
Nearest to egypt: egyptian, promised, ancient, homage, boondocks, cataphract, sars, bloodthirsty,
Epoch 3/10 Iteration: 28100 Avg. Training loss: 4.1066 0.1523 sec/batch
Epoch 3/10 Iteration: 28200 Avg. Training loss: 4.0840 0.1496 sec/batch
Epoch 3/10 Iteration: 28300 Avg. Training loss: 4.0645 0.1494 sec/batch
Epoch 3/10 Iteration: 28400 Avg. Training loss: 3.9674 0.1496 sec/batch
Epoch 3/10 Iteration: 28500 Avg. Training loss: 4.0818 0.1501 sec/batch
Epoch 3/10 Iteration: 28600 Avg. Training loss: 3.9762 0.1499 sec/batch
Epoch 3/10 Iteration: 28700 Avg. Training loss: 4.0719 0.1500 sec/batch
Epoch 3/10 Iteration: 28800 Avg. Training loss: 4.0688 0.1502 sec/batch
Epoch 3/10 Iteration: 28900 Avg. Training loss: 4.0505 0.1554 sec/batch
Epoch 3/10 Iteration: 29000 Avg. Training loss: 4.0931 0.1498 sec/batch
Nearest to however: overdue, pdpa, participates, shogun, goiter, reviewers, sacco, moves,
Nearest to no: longer, there, keck, cytokine, washtenaw, isonzo, eelam, acrimonious,
Nearest to were: brasenose, they, printed, stolen, these, benchers, sensational, lemnian,
Nearest to two: nov, ergodic, equ, guarantees, spender, sheng, nc, folic,
Nearest to called: so, sometimes, investigates, infinitude, retinal, bundestag, galerius, flaccus,
Nearest to new: york, zealand, papua, pluralism, seek, rename, caledonia, suffect,
Nearest to as: well, such, known, referred, taft, result, regarded, unintelligent,
Nearest to would: triadic, fluency, seem, dweezil, peart, be, flatten, berlusconi,
Nearest to frac: right, cdot, sin, dt, omega, mathrm, left, zam,
Nearest to defense: vultures, tuan, staff, responsibility, morecambe, kune, warship, forces,
Nearest to construction: elimination, materials, curtain, revise, pragmatic, ilyich, frobisher, documenting,
Nearest to engine: petrol, combustion, engines, origami, regents, chassis, steering, bullet,
Nearest to consists: vaughan, nacional, magistrate, arne, anchorage, bottle, henceforward, dialectics,
Nearest to existence: irrational, exacting, lorry, overlap, shimura, enslave, baptise, tilapia,
Nearest to troops: retaliation, freed, azores, successfully, sarin, sanatorium, justified, distraction,
Nearest to egypt: egyptian, promised, ancient, cataphract, sars, bloodthirsty, boondocks, postscript,
Epoch 3/10 Iteration: 29100 Avg. Training loss: 3.9930 0.1523 sec/batch
Epoch 3/10 Iteration: 29200 Avg. Training loss: 4.0600 0.1499 sec/batch
Epoch 3/10 Iteration: 29300 Avg. Training loss: 3.9907 0.1514 sec/batch
Epoch 3/10 Iteration: 29400 Avg. Training loss: 4.0524 0.1508 sec/batch
Epoch 3/10 Iteration: 29500 Avg. Training loss: 4.0590 0.1496 sec/batch
Epoch 3/10 Iteration: 29600 Avg. Training loss: 3.9597 0.1501 sec/batch
Epoch 3/10 Iteration: 29700 Avg. Training loss: 4.0944 0.1501 sec/batch
Epoch 3/10 Iteration: 29800 Avg. Training loss: 4.0341 0.1514 sec/batch
Epoch 3/10 Iteration: 29900 Avg. Training loss: 4.0401 0.1494 sec/batch
Epoch 3/10 Iteration: 30000 Avg. Training loss: 3.9595 0.1497 sec/batch
Nearest to however: overdue, participates, pdpa, shogun, goiter, taiko, reviewers, caucasians,
Nearest to no: longer, there, cytokine, keck, washtenaw, peltier, cheapest, oo,
Nearest to were: they, brasenose, printed, municipalities, these, benchers, stolen, frankfurter,
Nearest to two: ergodic, nov, spender, guarantees, equ, sheng, folic, nc,
Nearest to called: so, sometimes, investigates, infinitude, bundestag, retinal, galerius, flaccus,
Nearest to new: york, zealand, papua, seek, caledonia, rename, ny, pluralism,
Nearest to as: well, such, known, referred, taft, result, regarded, verhoeven,
Nearest to would: triadic, fluency, seem, be, peart, dweezil, goto, berlusconi,
Nearest to frac: right, cdot, sin, dt, mathrm, zam, omega, left,
Nearest to defense: vultures, responsibility, tuan, staff, morecambe, forces, warship, expenditures,
Nearest to construction: elimination, materials, ilyich, curtain, pragmatic, cut, frobisher, revise,
Nearest to engine: petrol, combustion, engines, origami, regents, chassis, steering, cordobas,
Nearest to consists: nacional, vaughan, magistrate, arne, tachelhit, anchorage, nola, henceforward,
Nearest to existence: irrational, exacting, lorry, overlap, shimura, enslave, baptise, tagus,
Nearest to troops: retaliation, freed, azores, successfully, sanatorium, sarin, allies, glanville,
Nearest to egypt: egyptian, promised, ancient, sars, cataphract, bloodthirsty, boondocks, postscript,
Epoch 3/10 Iteration: 30100 Avg. Training loss: 4.0640 0.1533 sec/batch
Epoch 3/10 Iteration: 30200 Avg. Training loss: 4.0892 0.1494 sec/batch
Epoch 3/10 Iteration: 30300 Avg. Training loss: 4.0510 0.1496 sec/batch
Epoch 3/10 Iteration: 30400 Avg. Training loss: 4.0907 0.1513 sec/batch
Epoch 3/10 Iteration: 30500 Avg. Training loss: 4.0865 0.1510 sec/batch
Epoch 3/10 Iteration: 30600 Avg. Training loss: 3.9812 0.1509 sec/batch
Epoch 3/10 Iteration: 30700 Avg. Training loss: 4.0748 0.1499 sec/batch
Epoch 3/10 Iteration: 30800 Avg. Training loss: 4.0810 0.1495 sec/batch
Epoch 3/10 Iteration: 30900 Avg. Training loss: 4.0787 0.1493 sec/batch
Epoch 3/10 Iteration: 31000 Avg. Training loss: 4.0500 0.1493 sec/batch
Nearest to however: overdue, participates, pdpa, shogun, reviewers, moves, goiter, caucasians,
Nearest to no: longer, there, keck, cytokine, washtenaw, oo, cheapest, peltier,
Nearest to were: they, brasenose, printed, municipalities, these, benchers, stolen, urals,
Nearest to two: nov, ergodic, spender, equ, sheng, guarantees, photius, nc,
Nearest to called: so, sometimes, infinitude, investigates, retinal, galerius, bundestag, kirkcaldy,
Nearest to new: york, zealand, papua, caledonia, seek, rename, pluralism, ny,
Nearest to as: well, such, known, referred, taft, result, regarded, verhoeven,
Nearest to would: triadic, fluency, seem, peart, be, moranis, dweezil, otherwise,
Nearest to frac: right, cdot, dt, sin, omega, mathrm, zam, left,
Nearest to defense: vultures, responsibility, tuan, staff, morecambe, warship, kune, expenditures,
Nearest to construction: elimination, materials, ilyich, revise, curtain, pragmatic, frobisher, cut,
Nearest to engine: petrol, combustion, engines, regents, origami, chassis, cordobas, steering,
Nearest to consists: vaughan, nacional, magistrate, arne, tachelhit, anchorage, score, dialectics,
Nearest to existence: irrational, exacting, lorry, overlap, baptise, shimura, enslave, tilapia,
Nearest to troops: freed, retaliation, azores, sanatorium, successfully, sarin, justified, allies,
Nearest to egypt: egyptian, ancient, promised, sars, cataphract, boondocks, bloodthirsty, homage,
Epoch 3/10 Iteration: 31100 Avg. Training loss: 4.0925 0.1527 sec/batch
Epoch 3/10 Iteration: 31200 Avg. Training loss: 4.0482 0.1514 sec/batch
Epoch 3/10 Iteration: 31300 Avg. Training loss: 4.0767 0.1514 sec/batch
Epoch 3/10 Iteration: 31400 Avg. Training loss: 4.0459 0.1501 sec/batch
Epoch 3/10 Iteration: 31500 Avg. Training loss: 3.9684 0.1498 sec/batch
Epoch 3/10 Iteration: 31600 Avg. Training loss: 4.0315 0.1493 sec/batch
Epoch 3/10 Iteration: 31700 Avg. Training loss: 4.0307 0.1499 sec/batch
Epoch 3/10 Iteration: 31800 Avg. Training loss: 4.0460 0.1495 sec/batch
Epoch 3/10 Iteration: 31900 Avg. Training loss: 4.0272 0.1495 sec/batch
Epoch 3/10 Iteration: 32000 Avg. Training loss: 4.0959 0.1504 sec/batch
Nearest to however: overdue, participates, pdpa, shogun, goiter, reviewers, moves, asylum,
Nearest to no: longer, there, keck, cytokine, washtenaw, oo, cheapest, peltier,
Nearest to were: printed, they, brasenose, municipalities, benchers, sensational, stolen, frankfurter,
Nearest to two: nov, ergodic, equ, guarantees, spender, nc, folic, sheng,
Nearest to called: so, sometimes, investigates, infinitude, retinal, bundestag, flaccus, galerius,
Nearest to new: york, zealand, papua, caledonia, seek, pluralism, ny, rename,
Nearest to as: well, such, known, referred, result, taft, regarded, verhoeven,
Nearest to would: triadic, fluency, seem, peart, be, otherwise, goto, likely,
Nearest to frac: right, cdot, dt, omega, mathrm, zam, sin, prod,
Nearest to defense: vultures, responsibility, staff, tuan, morecambe, forces, warship, piety,
Nearest to construction: elimination, materials, ilyich, frobisher, revise, pragmatic, gloucestershire, curtain,
Nearest to engine: petrol, combustion, engines, chassis, origami, automobile, regents, cordobas,
Nearest to consists: magistrate, vaughan, nacional, arne, anchorage, tachelhit, score, henceforward,
Nearest to existence: exacting, irrational, lorry, baptise, overlap, enslave, shimura, tagus,
Nearest to troops: freed, retaliation, azores, sanatorium, sarin, successfully, joining, justified,
Nearest to egypt: egyptian, promised, ancient, cataphract, sars, boondocks, postscript, bloodthirsty,
Epoch 3/10 Iteration: 32100 Avg. Training loss: 3.9652 0.1523 sec/batch
Epoch 3/10 Iteration: 32200 Avg. Training loss: 3.8487 0.1498 sec/batch
Epoch 3/10 Iteration: 32300 Avg. Training loss: 3.9427 0.1497 sec/batch
Epoch 3/10 Iteration: 32400 Avg. Training loss: 3.9962 0.1495 sec/batch
Epoch 3/10 Iteration: 32500 Avg. Training loss: 4.0673 0.1497 sec/batch
Epoch 3/10 Iteration: 32600 Avg. Training loss: 4.0866 0.1495 sec/batch
Epoch 3/10 Iteration: 32700 Avg. Training loss: 4.0483 0.1497 sec/batch
Epoch 3/10 Iteration: 32800 Avg. Training loss: 4.1450 0.1501 sec/batch
Epoch 3/10 Iteration: 32900 Avg. Training loss: 4.1294 0.1496 sec/batch
Epoch 3/10 Iteration: 33000 Avg. Training loss: 4.1091 0.1497 sec/batch
Nearest to however: overdue, pdpa, goiter, participates, moves, reviewers, taiko, shogun,
Nearest to no: longer, there, keck, cytokine, washtenaw, oo, cheapest, isonzo,
Nearest to were: brasenose, they, printed, benchers, municipalities, these, stolen, sensational,
Nearest to two: nov, ergodic, spender, equ, sheng, guarantees, marsden, folic,
Nearest to called: so, sometimes, investigates, infinitude, retinal, bundestag, flaccus, kirkcaldy,
Nearest to new: york, zealand, papua, seek, caledonia, orleans, pluralism, ny,
Nearest to as: well, such, known, referred, result, taft, regarded, verhoeven,
Nearest to would: triadic, fluency, be, seem, peart, retinal, moranis, berlusconi,
Nearest to frac: right, cdot, dt, omega, mathrm, sin, zam, prod,
Nearest to defense: vultures, responsibility, staff, tuan, morecambe, piety, forces, pannonia,
Nearest to construction: elimination, ilyich, materials, frobisher, pragmatic, gloucestershire, revise, curtain,
Nearest to engine: petrol, combustion, engines, chassis, steering, origami, automobile, regents,
Nearest to consists: magistrate, vaughan, nacional, arne, anchorage, tachelhit, score, nola,
Nearest to existence: exacting, lorry, irrational, baptise, overlap, izanami, shimura, tilapia,
Nearest to troops: freed, azores, retaliation, sarin, justified, sanatorium, distraction, successfully,
Nearest to egypt: egyptian, promised, ancient, cataphract, sars, homage, bloodthirsty, boondocks,
Epoch 3/10 Iteration: 33100 Avg. Training loss: 4.0589 0.1525 sec/batch
Epoch 3/10 Iteration: 33200 Avg. Training loss: 3.9087 0.1494 sec/batch
Epoch 3/10 Iteration: 33300 Avg. Training loss: 4.0699 0.1498 sec/batch
Epoch 3/10 Iteration: 33400 Avg. Training loss: 4.0418 0.1502 sec/batch
Epoch 3/10 Iteration: 33500 Avg. Training loss: 4.0397 0.1501 sec/batch
Epoch 3/10 Iteration: 33600 Avg. Training loss: 4.0149 0.1500 sec/batch
Epoch 3/10 Iteration: 33700 Avg. Training loss: 4.1113 0.1499 sec/batch
Epoch 3/10 Iteration: 33800 Avg. Training loss: 4.0300 0.1496 sec/batch
Epoch 3/10 Iteration: 33900 Avg. Training loss: 3.9992 0.1498 sec/batch
Epoch 3/10 Iteration: 34000 Avg. Training loss: 3.8036 0.1504 sec/batch
Nearest to however: overdue, participates, goiter, pdpa, shogun, caucasians, taiko, moves,
Nearest to no: longer, there, cytokine, keck, washtenaw, cheapest, peltier, eelam,
Nearest to were: they, brasenose, printed, benchers, municipalities, symphonic, these, stolen,
Nearest to two: nov, ergodic, guarantees, folic, spender, equ, marsden, guinean,
Nearest to called: so, sometimes, infinitude, investigates, bundestag, retinal, kirkcaldy, flaccus,
Nearest to new: york, zealand, papua, orleans, caledonia, seek, disturbs, ny,
Nearest to as: well, such, known, referred, result, regarded, taft, verhoeven,
Nearest to would: fluency, triadic, peart, be, retinal, berlusconi, otherwise, seem,
Nearest to frac: right, cdot, sin, dt, omega, mathrm, zam, left,
Nearest to defense: vultures, responsibility, staff, tuan, forces, piety, enceladus, morecambe,
Nearest to construction: elimination, materials, ilyich, frobisher, pragmatic, curtain, gloucestershire, revise,
Nearest to engine: petrol, combustion, engines, steering, chassis, regents, origami, cordobas,
Nearest to consists: magistrate, nacional, vaughan, tachelhit, arne, anchorage, score, shenanigans,
Nearest to existence: exacting, lorry, irrational, baptise, overlap, izanami, tilapia, personages,
Nearest to troops: azores, freed, retaliation, sarin, sanatorium, justified, distraction, successfully,
Nearest to egypt: egyptian, promised, ancient, cataphract, sars, bloodthirsty, homage, boondocks,
Epoch 3/10 Iteration: 34100 Avg. Training loss: 4.0695 0.1526 sec/batch
Epoch 3/10 Iteration: 34200 Avg. Training loss: 4.0454 0.1495 sec/batch
Epoch 3/10 Iteration: 34300 Avg. Training loss: 3.9535 0.1499 sec/batch
Epoch 3/10 Iteration: 34400 Avg. Training loss: 4.0426 0.1498 sec/batch
Epoch 3/10 Iteration: 34500 Avg. Training loss: 4.0594 0.1499 sec/batch
Epoch 3/10 Iteration: 34600 Avg. Training loss: 4.0171 0.1495 sec/batch
Epoch 3/10 Iteration: 34700 Avg. Training loss: 4.0091 0.1497 sec/batch
Epoch 3/10 Iteration: 34800 Avg. Training loss: 4.0777 0.1500 sec/batch
Epoch 3/10 Iteration: 34900 Avg. Training loss: 4.0939 0.1500 sec/batch
Epoch 4/10 Iteration: 35000 Avg. Training loss: 4.0885 0.0303 sec/batch
Nearest to however: overdue, reviewers, participates, moves, goiter, sacco, shogun, pdpa,
Nearest to no: longer, there, cytokine, keck, washtenaw, cheapest, peltier, oo,
Nearest to were: they, printed, brasenose, benchers, stolen, sensational, symphonic, these,
Nearest to two: nov, ergodic, equ, folic, spender, wrestlemania, marsden, sheng,
Nearest to called: so, sometimes, infinitude, investigates, retinal, bundestag, kirkcaldy, japh,
Nearest to new: york, zealand, papua, orleans, caledonia, seek, ny, disturbs,
Nearest to as: well, such, known, referred, result, regarded, taft, verhoeven,
Nearest to would: triadic, fluency, be, peart, retinal, seem, dweezil, otherwise,
Nearest to frac: right, cdot, dt, mathrm, sin, omega, zam, left,
Nearest to defense: vultures, responsibility, staff, piety, morecambe, tuan, pannonia, forces,
Nearest to construction: elimination, materials, ilyich, frobisher, gloucestershire, curtain, pragmatic, revise,
Nearest to engine: petrol, combustion, engines, chassis, steering, regents, cordobas, reliability,
Nearest to consists: magistrate, vaughan, nacional, arne, tachelhit, score, anchorage, spaces,
Nearest to existence: lorry, irrational, exacting, baptise, izanami, overlap, personages, parallel,
Nearest to troops: freed, azores, retaliation, sanatorium, sarin, justified, distraction, tehran,
Nearest to egypt: egyptian, promised, ancient, bloodthirsty, sars, cataphract, boondocks, homage,
Epoch 4/10 Iteration: 35100 Avg. Training loss: 4.0521 0.1526 sec/batch
Epoch 4/10 Iteration: 35200 Avg. Training loss: 3.9945 0.1513 sec/batch
Epoch 4/10 Iteration: 35300 Avg. Training loss: 4.0764 0.1513 sec/batch
Epoch 4/10 Iteration: 35400 Avg. Training loss: 3.9958 0.1494 sec/batch
Epoch 4/10 Iteration: 35500 Avg. Training loss: 4.0094 0.1516 sec/batch
Epoch 4/10 Iteration: 35600 Avg. Training loss: 4.0471 0.1536 sec/batch
Epoch 4/10 Iteration: 35700 Avg. Training loss: 3.9898 0.1537 sec/batch
Epoch 4/10 Iteration: 35800 Avg. Training loss: 4.0064 0.1522 sec/batch
Epoch 4/10 Iteration: 35900 Avg. Training loss: 4.0436 0.1515 sec/batch
Epoch 4/10 Iteration: 36000 Avg. Training loss: 4.0460 0.1528 sec/batch
Nearest to however: overdue, participates, sacco, reviewers, moves, pdpa, goiter, shogun,
Nearest to no: longer, there, cytokine, keck, washtenaw, eelam, isonzo, peltier,
Nearest to were: they, printed, brasenose, benchers, stolen, bodies, municipalities, these,
Nearest to two: nov, ergodic, equ, spender, folic, sheng, wrestlemania, rc,
Nearest to called: so, sometimes, infinitude, investigates, retinal, galerius, flaccus, bundestag,
Nearest to new: york, zealand, papua, orleans, caledonia, rename, pluralism, seek,
Nearest to as: well, such, known, referred, regarded, result, taft, unintelligent,
Nearest to would: triadic, fluency, be, seem, retinal, peart, otherwise, goto,
Nearest to frac: right, cdot, dt, sin, mathrm, zam, omega, left,
Nearest to defense: vultures, tuan, responsibility, staff, kune, piety, morecambe, enceladus,
Nearest to construction: elimination, materials, ilyich, frobisher, pragmatic, revise, gloucestershire, spokes,
Nearest to engine: petrol, engines, combustion, chassis, steering, regents, cordobas, automobile,
Nearest to consists: magistrate, nacional, vaughan, arne, anchorage, score, tachelhit, shenanigans,
Nearest to existence: lorry, irrational, exacting, baptise, izanami, tilapia, personages, shimura,
Nearest to troops: freed, retaliation, azores, sarin, sanatorium, distraction, justified, glanville,
Nearest to egypt: egyptian, ancient, promised, cataphract, bloodthirsty, homage, sars, boondocks,
Epoch 4/10 Iteration: 36100 Avg. Training loss: 4.0059 0.1525 sec/batch
Epoch 4/10 Iteration: 36200 Avg. Training loss: 4.0382 0.1500 sec/batch
Epoch 4/10 Iteration: 36300 Avg. Training loss: 4.0553 0.1499 sec/batch
Epoch 4/10 Iteration: 36400 Avg. Training loss: 4.0400 0.1528 sec/batch
Epoch 4/10 Iteration: 36500 Avg. Training loss: 4.0327 0.1497 sec/batch
Epoch 4/10 Iteration: 36600 Avg. Training loss: 4.0330 0.1530 sec/batch
Epoch 4/10 Iteration: 36700 Avg. Training loss: 3.8745 0.1558 sec/batch
Epoch 4/10 Iteration: 36800 Avg. Training loss: 4.0667 0.1557 sec/batch
Epoch 4/10 Iteration: 36900 Avg. Training loss: 4.0594 0.1546 sec/batch
Epoch 4/10 Iteration: 37000 Avg. Training loss: 4.0613 0.1540 sec/batch
Nearest to however: overdue, participates, pdpa, sacco, reviewers, moves, goiter, shogun,
Nearest to no: longer, there, cytokine, keck, washtenaw, peltier, cheapest, eelam,
Nearest to were: they, printed, brasenose, benchers, stolen, these, sensational, municipalities,
Nearest to two: nov, ergodic, spender, equ, sheng, folic, rc, guarantees,
Nearest to called: so, sometimes, investigates, infinitude, retinal, galerius, flaccus, kirkcaldy,
Nearest to new: york, zealand, papua, caledonia, orleans, rename, testament, ny,
Nearest to as: well, such, known, referred, result, regarded, taft, unintelligent,
Nearest to would: triadic, fluency, otherwise, be, peart, retinal, seem, dweezil,
Nearest to frac: right, cdot, dt, sin, mathrm, zam, omega, left,
Nearest to defense: vultures, tuan, responsibility, kune, forces, piety, staff, pannonia,
Nearest to construction: elimination, materials, ilyich, gloucestershire, frobisher, pragmatic, revise, facility,
Nearest to engine: petrol, engines, combustion, chassis, regents, steering, cordobas, automobile,
Nearest to consists: magistrate, nacional, vaughan, arne, tachelhit, score, anchorage, consisting,
Nearest to existence: lorry, exacting, irrational, tilapia, baptise, izanami, overlap, shimura,
Nearest to troops: freed, retaliation, azores, sanatorium, distraction, sarin, justified, uppercamelcase,
Nearest to egypt: egyptian, ancient, promised, cataphract, bloodthirsty, sars, boondocks, homage,
Epoch 4/10 Iteration: 37100 Avg. Training loss: 4.0487 0.1573 sec/batch
Epoch 4/10 Iteration: 37200 Avg. Training loss: 4.0739 0.1653 sec/batch
Epoch 4/10 Iteration: 37300 Avg. Training loss: 4.0395 0.1522 sec/batch
Epoch 4/10 Iteration: 37400 Avg. Training loss: 4.0566 0.1613 sec/batch
Epoch 4/10 Iteration: 37500 Avg. Training loss: 4.0636 0.1547 sec/batch
Epoch 4/10 Iteration: 37600 Avg. Training loss: 4.0225 0.1520 sec/batch
Epoch 4/10 Iteration: 37700 Avg. Training loss: 4.0027 0.1507 sec/batch
Epoch 4/10 Iteration: 37800 Avg. Training loss: 3.9149 0.1554 sec/batch
Epoch 4/10 Iteration: 37900 Avg. Training loss: 3.9138 0.1595 sec/batch
Epoch 4/10 Iteration: 38000 Avg. Training loss: 4.0100 0.1579 sec/batch
Nearest to however: overdue, participates, sacco, pdpa, goiter, shogun, moves, taiko,
Nearest to no: longer, there, cytokine, keck, washtenaw, peltier, yes, cheapest,
Nearest to were: they, printed, benchers, brasenose, municipalities, bodies, stolen, sensational,
Nearest to two: nov, ergodic, folic, sheng, equ, guarantees, spender, bids,
Nearest to called: so, sometimes, investigates, infinitude, retinal, flaccus, galerius, kirkcaldy,
Nearest to new: york, zealand, papua, caledonia, orleans, pluralism, rename, testament,
Nearest to as: well, such, known, referred, result, regarded, taft, unintelligent,
Nearest to would: triadic, fluency, be, otherwise, seem, retinal, likely, dweezil,
Nearest to frac: right, cdot, dt, mathrm, sin, left, zam, omega,
Nearest to defense: vultures, responsibility, tuan, kune, staff, piety, forces, employed,
Nearest to construction: elimination, materials, ilyich, gloucestershire, frobisher, facility, pragmatic, building,
Nearest to engine: petrol, engines, combustion, chassis, regents, steering, automobile, cordobas,
Nearest to consists: magistrate, nacional, vaughan, tachelhit, arne, qurra, consisting, score,
Nearest to existence: lorry, exacting, irrational, tilapia, overlap, baptise, izanami, shimura,
Nearest to troops: freed, retaliation, sanatorium, azores, distraction, sarin, justified, uppercamelcase,
Nearest to egypt: egyptian, ancient, promised, cataphract, bloodthirsty, sars, boondocks, homage,
Epoch 4/10 Iteration: 38100 Avg. Training loss: 3.9963 0.1619 sec/batch
Epoch 4/10 Iteration: 38200 Avg. Training loss: 3.9776 0.1509 sec/batch
Epoch 4/10 Iteration: 38300 Avg. Training loss: 4.0159 0.1514 sec/batch
Epoch 4/10 Iteration: 38400 Avg. Training loss: 4.0329 0.1503 sec/batch
Epoch 4/10 Iteration: 38500 Avg. Training loss: 3.9039 0.1527 sec/batch
Epoch 4/10 Iteration: 38600 Avg. Training loss: 4.0465 0.1534 sec/batch
Epoch 4/10 Iteration: 38700 Avg. Training loss: 4.0449 0.1499 sec/batch
Epoch 4/10 Iteration: 38800 Avg. Training loss: 4.0338 0.1528 sec/batch
Epoch 4/10 Iteration: 38900 Avg. Training loss: 4.0341 0.1493 sec/batch
Epoch 4/10 Iteration: 39000 Avg. Training loss: 4.0267 0.1514 sec/batch
Nearest to however: overdue, participates, sacco, shogun, moves, reviewers, pdpa, goiter,
Nearest to no: longer, there, cytokine, keck, yes, washtenaw, cheapest, peltier,
Nearest to were: they, printed, brasenose, stolen, benchers, bodies, tailless, these,
Nearest to two: nov, ergodic, folic, sheng, equ, spender, guarantees, bids,
Nearest to called: so, sometimes, investigates, infinitude, retinal, also, kirkcaldy, galerius,
Nearest to new: york, zealand, papua, orleans, caledonia, ny, testament, rename,
Nearest to as: well, such, known, referred, result, regarded, taft, unintelligent,
Nearest to would: triadic, fluency, be, otherwise, likely, retinal, seem, dweezil,
Nearest to frac: right, cdot, left, dt, mathrm, omega, zam, sin,
Nearest to defense: vultures, tuan, responsibility, piety, staff, kune, warship, morecambe,
Nearest to construction: elimination, ilyich, materials, pragmatic, frobisher, gloucestershire, revise, facility,
Nearest to engine: petrol, engines, combustion, chassis, steering, automobile, regents, cordobas,
Nearest to consists: magistrate, nacional, vaughan, score, arne, spaces, tachelhit, shenanigans,
Nearest to existence: lorry, exacting, irrational, tilapia, baptise, personages, overlap, shimura,
Nearest to troops: freed, retaliation, sanatorium, distraction, azores, sarin, uppercamelcase, joining,
Nearest to egypt: egyptian, ancient, promised, cataphract, sars, bloodthirsty, postscript, boondocks,
Epoch 4/10 Iteration: 39100 Avg. Training loss: 4.0381 0.1532 sec/batch
Epoch 4/10 Iteration: 39200 Avg. Training loss: 4.0591 0.1535 sec/batch
Epoch 4/10 Iteration: 39300 Avg. Training loss: 3.9725 0.1510 sec/batch
Epoch 4/10 Iteration: 39400 Avg. Training loss: 4.0338 0.1512 sec/batch
Epoch 4/10 Iteration: 39500 Avg. Training loss: 3.9927 0.1503 sec/batch
Epoch 4/10 Iteration: 39600 Avg. Training loss: 4.0369 0.1507 sec/batch
Epoch 4/10 Iteration: 39700 Avg. Training loss: 4.0455 0.1503 sec/batch
Epoch 4/10 Iteration: 39800 Avg. Training loss: 4.0609 0.1527 sec/batch
Epoch 4/10 Iteration: 39900 Avg. Training loss: 4.0730 0.1509 sec/batch
Epoch 4/10 Iteration: 40000 Avg. Training loss: 3.9982 0.1501 sec/batch
Nearest to however: overdue, participates, reviewers, taiko, sacco, pdpa, shogun, moves,
Nearest to no: longer, there, cytokine, keck, yes, washtenaw, indication, cheapest,
Nearest to were: they, printed, brasenose, stolen, tailless, bodies, these, benchers,
Nearest to two: ergodic, nov, sheng, folic, equ, spender, bids, guarantees,
Nearest to called: so, sometimes, investigates, infinitude, retinal, japh, kirkcaldy, also,
Nearest to new: york, zealand, papua, caledonia, orleans, ny, testament, seek,
Nearest to as: well, such, referred, known, regarded, result, taft, unintelligent,
Nearest to would: triadic, otherwise, fluency, be, peart, seem, likely, dweezil,
Nearest to frac: right, cdot, sin, zam, left, omega, dt, mathrm,
Nearest to defense: vultures, responsibility, tuan, staff, piety, kune, morecambe, employed,
Nearest to construction: elimination, ilyich, materials, gloucestershire, frobisher, pragmatic, spokes, brearley,
Nearest to engine: petrol, engines, combustion, chassis, steering, automobile, cordobas, regents,
Nearest to consists: magistrate, vaughan, nacional, score, arne, tachelhit, spaces, consisting,
Nearest to existence: lorry, exacting, irrational, tilapia, overlap, izanami, shimura, personages,
Nearest to troops: freed, retaliation, sanatorium, azores, sarin, distraction, joining, uppercamelcase,
Nearest to egypt: egyptian, ancient, promised, cataphract, sars, postscript, bloodthirsty, boondocks,
Epoch 4/10 Iteration: 40100 Avg. Training loss: 3.9329 0.1547 sec/batch
Epoch 4/10 Iteration: 40200 Avg. Training loss: 4.0193 0.1509 sec/batch
Epoch 4/10 Iteration: 40300 Avg. Training loss: 3.9850 0.1501 sec/batch
Epoch 4/10 Iteration: 40400 Avg. Training loss: 4.0483 0.1509 sec/batch
Epoch 4/10 Iteration: 40500 Avg. Training loss: 4.0323 0.1525 sec/batch
Epoch 4/10 Iteration: 40600 Avg. Training loss: 4.0263 0.1504 sec/batch
Epoch 4/10 Iteration: 40700 Avg. Training loss: 4.0322 0.1506 sec/batch
Epoch 4/10 Iteration: 40800 Avg. Training loss: 3.9643 0.1506 sec/batch
Epoch 4/10 Iteration: 40900 Avg. Training loss: 4.0086 0.1519 sec/batch
Epoch 4/10 Iteration: 41000 Avg. Training loss: 3.9715 0.1502 sec/batch
Nearest to however: overdue, participates, reviewers, sacco, taiko, pdpa, shogun, goiter,
Nearest to no: longer, there, keck, cytokine, yes, indication, washtenaw, isonzo,
Nearest to were: they, printed, brasenose, stolen, bodies, these, symphonic, municipalities,
Nearest to two: nov, ergodic, folic, sheng, equ, spender, guarantees, bids,
Nearest to called: so, sometimes, investigates, infinitude, kirkcaldy, retinal, japh, stm,
Nearest to new: york, zealand, papua, orleans, caledonia, ny, testament, hampshire,
Nearest to as: well, such, referred, known, regarded, result, taft, unintelligent,
Nearest to would: triadic, fluency, otherwise, be, peart, moranis, seem, likely,
Nearest to frac: right, cdot, left, sin, dt, zam, mathrm, omega,
Nearest to defense: vultures, responsibility, piety, tuan, staff, morecambe, kune, employed,
Nearest to construction: ilyich, elimination, pragmatic, materials, frobisher, gloucestershire, brearley, building,
Nearest to engine: petrol, engines, combustion, automobile, chassis, steering, cordobas, regents,
Nearest to consists: magistrate, nacional, score, vaughan, arne, tachelhit, spaces, consisting,
Nearest to existence: lorry, irrational, exacting, tilapia, accumulation, personages, overlap, baptise,
Nearest to troops: freed, sanatorium, azores, retaliation, sarin, distraction, uppercamelcase, joining,
Nearest to egypt: egyptian, ancient, promised, postscript, sars, cataphract, walkways, bloodthirsty,
Epoch 4/10 Iteration: 41100 Avg. Training loss: 4.0399 0.1533 sec/batch
Epoch 4/10 Iteration: 41200 Avg. Training loss: 3.8742 0.1507 sec/batch
Epoch 4/10 Iteration: 41300 Avg. Training loss: 4.0644 0.1534 sec/batch
Epoch 4/10 Iteration: 41400 Avg. Training loss: 4.0467 0.1571 sec/batch
Epoch 4/10 Iteration: 41500 Avg. Training loss: 3.9825 0.1535 sec/batch
Epoch 4/10 Iteration: 41600 Avg. Training loss: 3.9779 0.1511 sec/batch
Epoch 4/10 Iteration: 41700 Avg. Training loss: 3.9851 0.1504 sec/batch
Epoch 4/10 Iteration: 41800 Avg. Training loss: 4.0102 0.1523 sec/batch
Epoch 4/10 Iteration: 41900 Avg. Training loss: 4.0237 0.1548 sec/batch
Epoch 4/10 Iteration: 42000 Avg. Training loss: 4.0310 0.1610 sec/batch
Nearest to however: overdue, taiko, participates, reviewers, sacco, pdpa, goiter, moves,
Nearest to no: longer, there, cytokine, keck, yes, unshielded, cheapest, washtenaw,
Nearest to were: they, printed, brasenose, benchers, bodies, these, stolen, municipalities,
Nearest to two: nov, ergodic, folic, sheng, spender, bids, equ, guarantees,
Nearest to called: so, sometimes, investigates, infinitude, retinal, kirkcaldy, emptying, stm,
Nearest to new: york, zealand, papua, orleans, caledonia, ny, testament, pluralism,
Nearest to as: well, such, known, referred, result, regarded, taft, unintelligent,
Nearest to would: otherwise, fluency, be, triadic, likely, seem, peart, goto,
Nearest to frac: right, cdot, sin, zam, left, dt, omega, mathrm,
Nearest to defense: vultures, responsibility, tuan, staff, morecambe, piety, pannonia, kune,
Nearest to construction: ilyich, elimination, materials, gloucestershire, pragmatic, frobisher, cut, building,
Nearest to engine: engines, petrol, combustion, chassis, automobile, cordobas, steering, regents,
Nearest to consists: magistrate, score, vaughan, nacional, arne, tachelhit, spaces, consisting,
Nearest to existence: lorry, irrational, personages, exacting, tilapia, baptise, overlap, izanami,
Nearest to troops: freed, sanatorium, azores, retaliation, joining, sarin, distraction, papadopoulos,
Nearest to egypt: egyptian, ancient, promised, postscript, sars, cataphract, walkways, boondocks,
Epoch 4/10 Iteration: 42100 Avg. Training loss: 4.0657 0.1609 sec/batch
Epoch 4/10 Iteration: 42200 Avg. Training loss: 4.0307 0.1505 sec/batch
Epoch 4/10 Iteration: 42300 Avg. Training loss: 3.9747 0.1543 sec/batch
Epoch 4/10 Iteration: 42400 Avg. Training loss: 4.0604 0.1616 sec/batch
Epoch 4/10 Iteration: 42500 Avg. Training loss: 4.0434 0.1599 sec/batch
Epoch 4/10 Iteration: 42600 Avg. Training loss: 4.0129 0.1532 sec/batch
Epoch 4/10 Iteration: 42700 Avg. Training loss: 4.0287 0.1560 sec/batch
Epoch 4/10 Iteration: 42800 Avg. Training loss: 4.0468 0.1637 sec/batch
Epoch 4/10 Iteration: 42900 Avg. Training loss: 4.0360 0.1600 sec/batch
Epoch 4/10 Iteration: 43000 Avg. Training loss: 4.0406 0.1562 sec/batch
Nearest to however: overdue, participates, taiko, sacco, pdpa, reviewers, moves, caucasians,
Nearest to no: longer, there, cytokine, keck, yes, unshielded, cheapest, indication,
Nearest to were: they, bodies, printed, brasenose, stolen, benchers, municipalities, these,
Nearest to two: nov, ergodic, folic, sheng, bids, spender, guarantees, equ,
Nearest to called: so, sometimes, investigates, infinitude, retinal, kirkcaldy, affectionately, gravestone,
Nearest to new: york, zealand, papua, orleans, caledonia, testament, ny, pluralism,
Nearest to as: well, such, known, referred, regarded, result, taft, unintelligent,
Nearest to would: otherwise, fluency, be, triadic, peart, seem, likely, later,
Nearest to frac: right, cdot, sin, omega, zam, dt, mathrm, left,
Nearest to defense: vultures, responsibility, tuan, morecambe, piety, staff, employed, kune,
Nearest to construction: elimination, gloucestershire, ilyich, materials, frobisher, spokes, facility, pragmatic,
Nearest to engine: engines, petrol, combustion, automobile, chassis, steering, cordobas, regents,
Nearest to consists: magistrate, score, vaughan, nacional, consisting, consist, dactylic, arne,
Nearest to existence: lorry, irrational, personages, exacting, tilapia, baptise, overlap, tagus,
Nearest to troops: freed, sanatorium, azores, joining, distraction, glanville, sarin, retaliation,
Nearest to egypt: egyptian, ancient, promised, postscript, sars, cataphract, walkways, marchibroda,
Epoch 4/10 Iteration: 43100 Avg. Training loss: 3.9780 0.1557 sec/batch
Epoch 4/10 Iteration: 43200 Avg. Training loss: 3.9453 0.1560 sec/batch
Epoch 4/10 Iteration: 43300 Avg. Training loss: 3.9861 0.1522 sec/batch
Epoch 4/10 Iteration: 43400 Avg. Training loss: 4.0164 0.1531 sec/batch
Epoch 4/10 Iteration: 43500 Avg. Training loss: 4.0149 0.1512 sec/batch
Epoch 4/10 Iteration: 43600 Avg. Training loss: 3.9538 0.1525 sec/batch
Epoch 4/10 Iteration: 43700 Avg. Training loss: 4.0706 0.1534 sec/batch
Epoch 4/10 Iteration: 43800 Avg. Training loss: 3.8192 0.1557 sec/batch
Epoch 4/10 Iteration: 43900 Avg. Training loss: 3.9231 0.1584 sec/batch
Epoch 4/10 Iteration: 44000 Avg. Training loss: 3.8564 0.1536 sec/batch
Nearest to however: overdue, taiko, sacco, pdpa, participates, reviewers, scholars, shogun,
Nearest to no: longer, there, cytokine, keck, unshielded, yes, washtenaw, indication,
Nearest to were: they, bodies, stolen, printed, benchers, brasenose, these, municipalities,
Nearest to two: spender, nov, equ, marsden, sheng, jafar, ergodic, garret,
Nearest to called: so, sometimes, investigates, infinitude, kirkcaldy, flaccus, affectionately, stm,
Nearest to new: york, zealand, papua, caledonia, orleans, ny, jersey, testament,
Nearest to as: well, such, known, referred, regarded, result, taft, verhoeven,
Nearest to would: otherwise, be, fluency, later, likely, triadic, moranis, peart,
Nearest to frac: right, cdot, sin, dt, zam, mathrm, omega, left,
Nearest to defense: vultures, responsibility, piety, tuan, staff, morecambe, employed, pannonia,
Nearest to construction: elimination, gloucestershire, ilyich, projects, frobisher, spokes, materials, facility,
Nearest to engine: engines, petrol, combustion, automobile, chassis, steering, regents, cordobas,
Nearest to consists: magistrate, vaughan, score, consisting, arne, nacional, tachelhit, consist,
Nearest to existence: lorry, irrational, personages, exacting, baptise, tilapia, tagus, overlap,
Nearest to troops: freed, sanatorium, azores, sarin, clad, joining, glanville, feeder,
Nearest to egypt: egyptian, ancient, promised, postscript, sars, cataphract, walkways, marchibroda,
Epoch 4/10 Iteration: 44100 Avg. Training loss: 4.0132 0.1569 sec/batch
Epoch 4/10 Iteration: 44200 Avg. Training loss: 4.0529 0.1523 sec/batch
Epoch 4/10 Iteration: 44300 Avg. Training loss: 4.0799 0.1560 sec/batch
Epoch 4/10 Iteration: 44400 Avg. Training loss: 4.0176 0.1585 sec/batch
Epoch 4/10 Iteration: 44500 Avg. Training loss: 4.0837 0.1562 sec/batch
Epoch 4/10 Iteration: 44600 Avg. Training loss: 4.0942 0.1574 sec/batch
Epoch 4/10 Iteration: 44700 Avg. Training loss: 4.0458 0.1587 sec/batch
Epoch 4/10 Iteration: 44800 Avg. Training loss: 3.9930 0.1607 sec/batch
Epoch 4/10 Iteration: 44900 Avg. Training loss: 3.9188 0.1575 sec/batch
Epoch 4/10 Iteration: 45000 Avg. Training loss: 4.0061 0.1533 sec/batch
Nearest to however: overdue, taiko, sacco, pdpa, participates, reviewers, scholars, goiter,
Nearest to no: longer, there, cytokine, keck, indication, yes, unshielded, cheapest,
Nearest to were: they, benchers, bodies, stolen, brasenose, tailless, printed, incarcerated,
Nearest to two: nov, spender, ergodic, folic, bids, equ, guarantees, sheng,
Nearest to called: so, sometimes, investigates, kirkcaldy, infinitude, japh, stm, also,
Nearest to new: york, zealand, papua, orleans, caledonia, jersey, testament, ny,
Nearest to as: well, such, known, referred, result, regarded, taft, unintelligent,
Nearest to would: otherwise, fluency, be, later, triadic, seem, peart, goto,
Nearest to frac: right, cdot, sin, dt, left, zam, mathrm, schwarzschild,
Nearest to defense: vultures, responsibility, tuan, morecambe, piety, pannonia, employed, staff,
Nearest to construction: elimination, ilyich, materials, projects, gloucestershire, pragmatic, facility, frobisher,
Nearest to engine: engines, petrol, combustion, steering, automobile, chassis, regents, cordobas,
Nearest to consists: magistrate, score, consisting, vaughan, nacional, consist, arne, tachelhit,
Nearest to existence: lorry, personages, exacting, irrational, tilapia, tagus, baptise, accumulation,
Nearest to troops: freed, azores, sanatorium, sarin, retaliation, joining, distraction, uppercamelcase,
Nearest to egypt: egyptian, promised, ancient, postscript, sars, cataphract, walkways, bloodthirsty,
Epoch 4/10 Iteration: 45100 Avg. Training loss: 3.9795 0.1551 sec/batch
Epoch 4/10 Iteration: 45200 Avg. Training loss: 4.0656 0.1546 sec/batch
Epoch 4/10 Iteration: 45300 Avg. Training loss: 3.9773 0.1596 sec/batch
Epoch 4/10 Iteration: 45400 Avg. Training loss: 4.0588 0.1582 sec/batch
Epoch 4/10 Iteration: 45500 Avg. Training loss: 4.0120 0.1600 sec/batch
Epoch 4/10 Iteration: 45600 Avg. Training loss: 3.8817 0.1543 sec/batch
Epoch 4/10 Iteration: 45700 Avg. Training loss: 3.8174 0.1548 sec/batch
Epoch 4/10 Iteration: 45800 Avg. Training loss: 4.0454 0.1514 sec/batch
Epoch 4/10 Iteration: 45900 Avg. Training loss: 3.9556 0.1523 sec/batch
Epoch 4/10 Iteration: 46000 Avg. Training loss: 3.9834 0.1506 sec/batch
Nearest to however: overdue, taiko, participates, sacco, reviewers, pdpa, dour, scholars,
Nearest to no: longer, there, cytokine, yes, keck, indication, cheapest, unshielded,
Nearest to were: they, bodies, benchers, printed, stolen, tailless, brasenose, these,
Nearest to two: nov, equ, spender, ergodic, sheng, marsden, folic, lazzeri,
Nearest to called: so, sometimes, investigates, infinitude, kirkcaldy, japh, stm, also,
Nearest to new: york, zealand, papua, orleans, caledonia, jersey, ny, testament,
Nearest to as: well, such, known, referred, regarded, result, taft, unintelligent,
Nearest to would: otherwise, fluency, be, triadic, later, peart, seem, haldane,
Nearest to frac: right, cdot, sin, zam, left, schwarzschild, dt, prod,
Nearest to defense: vultures, responsibility, piety, tuan, kune, morecambe, staff, employed,
Nearest to construction: elimination, materials, ilyich, gloucestershire, projects, frobisher, facility, pragmatic,
Nearest to engine: engines, petrol, combustion, automobile, steering, cordobas, regents, chassis,
Nearest to consists: magistrate, consisting, score, vaughan, consist, nacional, tachelhit, composed,
Nearest to existence: personages, irrational, lorry, exacting, tilapia, ant, parallel, provability,
Nearest to troops: freed, sanatorium, azores, sarin, retaliation, ludovico, glanville, tehran,
Nearest to egypt: egyptian, promised, ancient, postscript, cataphract, sars, walkways, marchibroda,
Epoch 4/10 Iteration: 46100 Avg. Training loss: 4.0460 0.1562 sec/batch
Epoch 4/10 Iteration: 46200 Avg. Training loss: 4.0192 0.1566 sec/batch
Epoch 4/10 Iteration: 46300 Avg. Training loss: 3.9465 0.1522 sec/batch
Epoch 4/10 Iteration: 46400 Avg. Training loss: 4.0405 0.1559 sec/batch
Epoch 4/10 Iteration: 46500 Avg. Training loss: 4.0127 0.1547 sec/batch
Epoch 4/10 Iteration: 46600 Avg. Training loss: 4.0863 0.1575 sec/batch
Epoch 5/10 Iteration: 46700 Avg. Training loss: 4.0490 0.0919 sec/batch
Epoch 5/10 Iteration: 46800 Avg. Training loss: 3.9664 0.1519 sec/batch
Epoch 5/10 Iteration: 46900 Avg. Training loss: 4.0156 0.1531 sec/batch
Epoch 5/10 Iteration: 47000 Avg. Training loss: 4.0226 0.1525 sec/batch
Nearest to however: overdue, taiko, sacco, reviewers, participates, pdpa, goiter, scholars,
Nearest to no: longer, there, cytokine, yes, keck, consensus, cheapest, unshielded,
Nearest to were: they, bodies, stolen, benchers, brasenose, printed, tailless, sensational,
Nearest to two: nov, folic, sheng, ergodic, spender, equ, bids, lazzeri,
Nearest to called: so, sometimes, investigates, infinitude, kirkcaldy, affectionately, retinal, japh,
Nearest to new: york, zealand, papua, orleans, caledonia, jersey, ny, testament,
Nearest to as: well, such, known, referred, result, regarded, taft, unintelligent,
Nearest to would: otherwise, be, later, fluency, triadic, hoped, seem, peart,
Nearest to frac: right, cdot, zam, sin, schwarzschild, left, dt, prod,
Nearest to defense: vultures, tuan, kune, responsibility, piety, morecambe, employed, staff,
Nearest to construction: elimination, materials, projects, gloucestershire, ilyich, frobisher, facility, spokes,
Nearest to engine: petrol, engines, combustion, automobile, steering, regents, cordobas, chassis,
Nearest to consists: magistrate, consisting, score, consist, vaughan, nacional, arne, spaces,
Nearest to existence: irrational, personages, lorry, baptise, tilapia, exacting, provability, parallel,
Nearest to troops: freed, sanatorium, sarin, azores, clad, distraction, retaliation, glanville,
Nearest to egypt: egyptian, ancient, promised, cataphract, postscript, sars, height, bloodthirsty,
Epoch 5/10 Iteration: 47100 Avg. Training loss: 3.9495 0.1574 sec/batch
Epoch 5/10 Iteration: 47200 Avg. Training loss: 3.9934 0.1507 sec/batch
Epoch 5/10 Iteration: 47300 Avg. Training loss: 3.9709 0.1497 sec/batch
Epoch 5/10 Iteration: 47400 Avg. Training loss: 3.9923 0.1524 sec/batch
Epoch 5/10 Iteration: 47500 Avg. Training loss: 4.0011 0.1545 sec/batch
Epoch 5/10 Iteration: 47600 Avg. Training loss: 4.0143 0.1539 sec/batch
Epoch 5/10 Iteration: 47700 Avg. Training loss: 3.9883 0.1511 sec/batch
Epoch 5/10 Iteration: 47800 Avg. Training loss: 3.9738 0.1512 sec/batch
Epoch 5/10 Iteration: 47900 Avg. Training loss: 4.0614 0.1535 sec/batch
Epoch 5/10 Iteration: 48000 Avg. Training loss: 3.9982 0.1561 sec/batch
Nearest to however: overdue, sacco, participates, taiko, reviewers, pdpa, scholars, shogun,
Nearest to no: longer, there, cytokine, yes, keck, indication, washtenaw, consensus,
Nearest to were: they, bodies, stolen, printed, benchers, brasenose, tailless, sensational,
Nearest to two: nov, folic, ergodic, equ, spender, sheng, jafar, biel,
Nearest to called: so, sometimes, investigates, infinitude, flaccus, kirkcaldy, retinal, stm,
Nearest to new: york, zealand, papua, orleans, caledonia, jersey, testament, ny,
Nearest to as: well, such, known, referred, result, regarded, taft, unintelligent,
Nearest to would: otherwise, be, fluency, triadic, later, hoped, seem, peart,
Nearest to frac: right, cdot, sin, zam, left, dt, schwarzschild, prod,
Nearest to defense: vultures, tuan, responsibility, kune, piety, morecambe, pannonia, aide,
Nearest to construction: elimination, materials, projects, gloucestershire, ilyich, frobisher, facility, building,
Nearest to engine: petrol, engines, combustion, automobile, chassis, regents, cordobas, steering,
Nearest to consists: magistrate, consisting, score, consist, vaughan, nacional, arne, spaces,
Nearest to existence: lorry, personages, tilapia, irrational, ant, baptise, exacting, provability,
Nearest to troops: sanatorium, freed, sarin, azores, retaliation, clad, glanville, distraction,
Nearest to egypt: egyptian, ancient, promised, cataphract, height, bloodthirsty, postscript, marchibroda,
Epoch 5/10 Iteration: 48100 Avg. Training loss: 4.0097 0.1590 sec/batch
Epoch 5/10 Iteration: 48200 Avg. Training loss: 4.0154 0.1516 sec/batch
Epoch 5/10 Iteration: 48300 Avg. Training loss: 3.9471 0.1534 sec/batch
Epoch 5/10 Iteration: 48400 Avg. Training loss: 3.9197 0.1553 sec/batch
Epoch 5/10 Iteration: 48500 Avg. Training loss: 4.0131 0.1545 sec/batch
Epoch 5/10 Iteration: 48600 Avg. Training loss: 4.0443 0.1518 sec/batch
Epoch 5/10 Iteration: 48700 Avg. Training loss: 4.0246 0.1530 sec/batch
Epoch 5/10 Iteration: 48800 Avg. Training loss: 4.0464 0.1591 sec/batch
Epoch 5/10 Iteration: 48900 Avg. Training loss: 4.0312 0.1515 sec/batch
Epoch 5/10 Iteration: 49000 Avg. Training loss: 4.0314 0.1509 sec/batch
Nearest to however: overdue, sacco, participates, taiko, pdpa, reviewers, scholars, dogger,
Nearest to no: longer, there, cytokine, yes, keck, indication, cheapest, washtenaw,
Nearest to were: they, stolen, brasenose, benchers, bodies, tailless, printed, sensational,
Nearest to two: nov, sheng, spender, bids, folic, biel, ergodic, rc,
Nearest to called: so, sometimes, investigates, kirkcaldy, affectionately, flaccus, infinitude, also,
Nearest to new: york, zealand, papua, orleans, testament, caledonia, jersey, radically,
Nearest to as: well, such, referred, known, result, regarded, taft, unintelligent,
Nearest to would: otherwise, be, later, fluency, hoped, triadic, moranis, seem,
Nearest to frac: right, cdot, sin, zam, left, schwarzschild, prod, dt,
Nearest to defense: vultures, kune, tuan, responsibility, piety, employed, pannonia, morecambe,
Nearest to construction: elimination, materials, projects, gloucestershire, documenting, ilyich, frobisher, building,
Nearest to engine: petrol, engines, combustion, automobile, chassis, cordobas, regents, steering,
Nearest to consists: magistrate, consist, consisting, score, nacional, arne, vaughan, composed,
Nearest to existence: tilapia, lorry, personages, ant, irrational, exacting, baptise, tagus,
Nearest to troops: sanatorium, freed, sarin, clad, azores, uppercamelcase, glanville, retaliation,
Nearest to egypt: egyptian, ancient, promised, cataphract, height, bloodthirsty, marchibroda, postscript,
Epoch 5/10 Iteration: 49100 Avg. Training loss: 4.0460 0.1554 sec/batch
Epoch 5/10 Iteration: 49200 Avg. Training loss: 4.0280 0.1589 sec/batch
Epoch 5/10 Iteration: 49300 Avg. Training loss: 3.9668 0.1534 sec/batch
Epoch 5/10 Iteration: 49400 Avg. Training loss: 3.9601 0.1568 sec/batch
Epoch 5/10 Iteration: 49500 Avg. Training loss: 3.8243 0.1603 sec/batch
Epoch 5/10 Iteration: 49600 Avg. Training loss: 3.9638 0.1538 sec/batch
Epoch 5/10 Iteration: 49700 Avg. Training loss: 4.0215 0.1577 sec/batch
Epoch 5/10 Iteration: 49800 Avg. Training loss: 3.9398 0.1563 sec/batch
Epoch 5/10 Iteration: 49900 Avg. Training loss: 3.9776 0.1556 sec/batch
Epoch 5/10 Iteration: 50000 Avg. Training loss: 3.9606 0.1558 sec/batch
Nearest to however: overdue, participates, sacco, taiko, reviewers, scholars, pdpa, shogun,
Nearest to no: longer, there, yes, cytokine, keck, cheapest, indication, consensus,
Nearest to were: they, bodies, stolen, benchers, tailless, printed, brasenose, these,
Nearest to two: nov, folic, ergodic, sheng, equ, guarantees, spender, bids,
Nearest to called: so, sometimes, investigates, infinitude, stm, affectionately, also, retinal,
Nearest to new: york, zealand, papua, caledonia, orleans, testament, jersey, suffect,
Nearest to as: well, such, referred, known, result, regarded, taft, unintelligent,
Nearest to would: otherwise, be, fluency, triadic, later, retinal, hoped, seem,
Nearest to frac: right, cdot, left, sin, zam, schwarzschild, dt, prod,
Nearest to defense: vultures, kune, tuan, responsibility, pannonia, employed, piety, aide,
Nearest to construction: elimination, projects, materials, frobisher, gloucestershire, building, ilyich, facility,
Nearest to engine: petrol, engines, combustion, automobile, chassis, cordobas, regents, airframe,
Nearest to consists: magistrate, consisting, consist, nacional, score, spaces, cour, composed,
Nearest to existence: personages, tilapia, lorry, irrational, exacting, baptise, ant, accumulation,
Nearest to troops: sanatorium, freed, sarin, clad, azores, glanville, feeder, uppercamelcase,
Nearest to egypt: egyptian, promised, ancient, cataphract, bloodthirsty, height, walkways, marchibroda,
Epoch 5/10 Iteration: 50100 Avg. Training loss: 3.8853 0.1605 sec/batch
Epoch 5/10 Iteration: 50200 Avg. Training loss: 4.0037 0.1529 sec/batch
Epoch 5/10 Iteration: 50300 Avg. Training loss: 4.0118 0.1553 sec/batch
Epoch 5/10 Iteration: 50400 Avg. Training loss: 4.0074 0.1616 sec/batch
Epoch 5/10 Iteration: 50500 Avg. Training loss: 4.0016 0.1576 sec/batch
Epoch 5/10 Iteration: 50600 Avg. Training loss: 4.0015 0.1527 sec/batch
Epoch 5/10 Iteration: 50700 Avg. Training loss: 3.9776 0.1535 sec/batch
Epoch 5/10 Iteration: 50800 Avg. Training loss: 4.0430 0.1540 sec/batch
Epoch 5/10 Iteration: 50900 Avg. Training loss: 4.0066 0.1533 sec/batch
Epoch 5/10 Iteration: 51000 Avg. Training loss: 3.9669 0.1540 sec/batch
Nearest to however: overdue, participates, taiko, sacco, reviewers, scholars, shogun, dogger,
Nearest to no: longer, there, yes, cytokine, keck, indication, consensus, unshielded,
Nearest to were: they, stolen, tailless, bodies, printed, benchers, brasenose, sensational,
Nearest to two: nov, sheng, folic, ergodic, spender, equ, biel, bids,
Nearest to called: so, sometimes, investigates, stm, kirkcaldy, koh, affectionately, spinach,
Nearest to new: york, zealand, papua, orleans, caledonia, jersey, testament, suffect,
Nearest to as: well, such, referred, known, result, regarded, taft, unintelligent,
Nearest to would: otherwise, later, hoped, be, moranis, fluency, triadic, announced,
Nearest to frac: right, cdot, left, sin, schwarzschild, zam, omega, dt,
Nearest to defense: vultures, kune, employed, pannonia, responsibility, tuan, morecambe, aide,
Nearest to construction: elimination, projects, materials, frobisher, ilyich, documenting, cellphone, building,
Nearest to engine: petrol, engines, combustion, automobile, chassis, airframe, regents, cordobas,
Nearest to consists: magistrate, consisting, consist, score, nacional, cour, composed, arne,
Nearest to existence: personages, tilapia, lorry, exacting, irrational, baptise, ant, tagus,
Nearest to troops: sanatorium, freed, sarin, clad, azores, needles, glanville, feeder,
Nearest to egypt: egyptian, promised, ancient, cataphract, bloodthirsty, marchibroda, height, postscript,
Epoch 5/10 Iteration: 51100 Avg. Training loss: 4.0025 0.1544 sec/batch
Epoch 5/10 Iteration: 51200 Avg. Training loss: 3.9749 0.1550 sec/batch
Epoch 5/10 Iteration: 51300 Avg. Training loss: 4.0140 0.1550 sec/batch
Epoch 5/10 Iteration: 51400 Avg. Training loss: 4.0200 0.1539 sec/batch
Epoch 5/10 Iteration: 51500 Avg. Training loss: 4.0531 0.1512 sec/batch
Epoch 5/10 Iteration: 51600 Avg. Training loss: 4.0206 0.1531 sec/batch
Epoch 5/10 Iteration: 51700 Avg. Training loss: 3.9290 0.1550 sec/batch
Epoch 5/10 Iteration: 51800 Avg. Training loss: 4.0045 0.1525 sec/batch
Epoch 5/10 Iteration: 51900 Avg. Training loss: 3.9051 0.1543 sec/batch
Epoch 5/10 Iteration: 52000 Avg. Training loss: 3.9992 0.1577 sec/batch
Nearest to however: overdue, participates, sacco, taiko, reviewers, scholars, pdpa, goiter,
Nearest to no: longer, there, yes, cytokine, keck, unshielded, indication, consensus,
Nearest to were: they, tailless, bodies, printed, stolen, benchers, brasenose, these,
Nearest to two: nov, equ, sheng, ergodic, folic, bids, spender, biel,
Nearest to called: so, sometimes, investigates, kirkcaldy, koh, affectionately, japh, stm,
Nearest to new: york, zealand, papua, orleans, caledonia, jersey, testament, ny,
Nearest to as: well, such, referred, known, result, regarded, taft, unintelligent,
Nearest to would: otherwise, be, triadic, fluency, hoped, later, seem, retinal,
Nearest to frac: right, cdot, left, sin, schwarzschild, mathrm, dt, zam,
Nearest to defense: vultures, responsibility, kune, pannonia, defence, employed, tuan, morecambe,
Nearest to construction: elimination, projects, materials, ilyich, documenting, cellphone, frobisher, gloucestershire,
Nearest to engine: petrol, engines, combustion, automobile, airframe, chassis, steering, regents,
Nearest to consists: magistrate, consisting, score, consist, nacional, vaughan, cour, spaces,
Nearest to existence: personages, tilapia, lorry, irrational, ant, provability, exacting, baptise,
Nearest to troops: sanatorium, freed, clad, sarin, glanville, azores, needles, retaliation,
Nearest to egypt: egyptian, promised, ancient, cataphract, postscript, marchibroda, bloodthirsty, walkways,
Epoch 5/10 Iteration: 52100 Avg. Training loss: 4.0198 0.1585 sec/batch
Epoch 5/10 Iteration: 52200 Avg. Training loss: 3.9855 0.1576 sec/batch
Epoch 5/10 Iteration: 52300 Avg. Training loss: 4.0313 0.1559 sec/batch
Epoch 5/10 Iteration: 52400 Avg. Training loss: 3.9680 0.1613 sec/batch
Epoch 5/10 Iteration: 52500 Avg. Training loss: 3.9674 0.1541 sec/batch
Epoch 5/10 Iteration: 52600 Avg. Training loss: 3.9595 0.1595 sec/batch
Epoch 5/10 Iteration: 52700 Avg. Training loss: 3.9650 0.1527 sec/batch
Epoch 5/10 Iteration: 52800 Avg. Training loss: 4.0335 0.1553 sec/batch
Epoch 5/10 Iteration: 52900 Avg. Training loss: 3.8715 0.1519 sec/batch
Epoch 5/10 Iteration: 53000 Avg. Training loss: 3.9986 0.1587 sec/batch
Nearest to however: overdue, participates, sacco, taiko, reviewers, pdpa, dour, caso,
Nearest to no: longer, there, yes, import, cytokine, unshielded, keck, exists,
Nearest to were: they, tailless, printed, bodies, stolen, benchers, these, soundtracks,
Nearest to two: equ, nov, sheng, bids, folic, ergodic, spender, guarantees,
Nearest to called: so, sometimes, investigates, kirkcaldy, often, japh, koh, infinitude,
Nearest to new: york, zealand, papua, orleans, caledonia, jersey, testament, ny,
Nearest to as: well, such, referred, known, result, regarded, taft, verhoeven,
Nearest to would: otherwise, be, triadic, likely, announced, later, seem, hoped,
Nearest to frac: right, cdot, sin, left, schwarzschild, zam, mathrm, dt,
Nearest to defense: vultures, responsibility, kune, defence, pannonia, piety, employed, tuan,
Nearest to construction: elimination, projects, frobisher, ilyich, cellphone, materials, gloucestershire, documenting,
Nearest to engine: petrol, engines, combustion, automobile, airframe, chassis, regents, cordobas,
Nearest to consists: magistrate, consisting, score, consist, nacional, cour, composed, extensive,
Nearest to existence: personages, tilapia, provability, lorry, irrational, ant, tagus, baptise,
Nearest to troops: sanatorium, freed, sarin, clad, glanville, azores, feeder, sieur,
Nearest to egypt: egyptian, promised, ancient, cataphract, marchibroda, walkways, height, postscript,
Epoch 5/10 Iteration: 53100 Avg. Training loss: 4.0292 0.1599 sec/batch
Epoch 5/10 Iteration: 53200 Avg. Training loss: 3.9809 0.1558 sec/batch
Epoch 5/10 Iteration: 53300 Avg. Training loss: 3.8527 0.1546 sec/batch
Epoch 5/10 Iteration: 53400 Avg. Training loss: 4.0267 0.1562 sec/batch
Epoch 5/10 Iteration: 53500 Avg. Training loss: 4.0302 0.1558 sec/batch
Epoch 5/10 Iteration: 53600 Avg. Training loss: 3.9867 0.1543 sec/batch
Epoch 5/10 Iteration: 53700 Avg. Training loss: 4.0078 0.1558 sec/batch
Epoch 5/10 Iteration: 53800 Avg. Training loss: 4.0590 0.1537 sec/batch
Epoch 5/10 Iteration: 53900 Avg. Training loss: 3.9757 0.1538 sec/batch
Epoch 5/10 Iteration: 54000 Avg. Training loss: 3.9800 0.1517 sec/batch
Nearest to however: overdue, participates, sacco, taiko, reviewers, pdpa, scholars, dogger,
Nearest to no: longer, there, yes, import, cytokine, unshielded, indication, keck,
Nearest to were: they, these, benchers, bodies, tailless, printed, rcaf, stolen,
Nearest to two: nov, equ, sheng, folic, bids, ergodic, spender, guarantees,
Nearest to called: so, sometimes, investigates, kirkcaldy, often, koh, emptying, affectionately,
Nearest to new: york, zealand, papua, orleans, caledonia, jersey, testament, radically,
Nearest to as: well, such, referred, known, result, regarded, taft, verhoeven,
Nearest to would: otherwise, later, be, seem, announced, hoped, moranis, triadic,
Nearest to frac: right, cdot, sin, left, schwarzschild, zam, dt, mathrm,
Nearest to defense: vultures, responsibility, defence, pannonia, employed, kune, aide, piety,
Nearest to construction: elimination, projects, frobisher, facility, materials, ilyich, gloucestershire, cellphone,
Nearest to engine: petrol, engines, combustion, automobile, chassis, regents, airframe, cordobas,
Nearest to consists: magistrate, consisting, score, consist, nacional, extensive, cour, vaughan,
Nearest to existence: personages, lorry, provability, ant, tilapia, tagus, baptise, irrational,
Nearest to troops: sanatorium, freed, sarin, clad, azores, glanville, feeder, papadopoulos,
Nearest to egypt: egyptian, ancient, promised, cataphract, postscript, marchibroda, height, walkways,
Epoch 5/10 Iteration: 54100 Avg. Training loss: 4.0416 0.1547 sec/batch
Epoch 5/10 Iteration: 54200 Avg. Training loss: 4.0026 0.1542 sec/batch
Epoch 5/10 Iteration: 54300 Avg. Training loss: 4.0033 0.1504 sec/batch
Epoch 5/10 Iteration: 54400 Avg. Training loss: 4.0268 0.1502 sec/batch
Epoch 5/10 Iteration: 54500 Avg. Training loss: 4.0098 0.1502 sec/batch
Epoch 5/10 Iteration: 54600 Avg. Training loss: 4.0227 0.1527 sec/batch
Epoch 5/10 Iteration: 54700 Avg. Training loss: 4.0025 0.1532 sec/batch
Epoch 5/10 Iteration: 54800 Avg. Training loss: 3.9217 0.1578 sec/batch
Epoch 5/10 Iteration: 54900 Avg. Training loss: 3.9825 0.1589 sec/batch
Epoch 5/10 Iteration: 55000 Avg. Training loss: 3.9836 0.1591 sec/batch
Nearest to however: overdue, participates, sacco, taiko, reviewers, scholars, dogger, reportage,
Nearest to no: longer, there, yes, import, cytokine, unshielded, exists, indication,
Nearest to were: they, bodies, printed, tailless, these, stolen, benchers, taa,
Nearest to two: nov, guarantees, equ, folic, ergodic, bids, sheng, spender,
Nearest to called: so, sometimes, investigates, kirkcaldy, often, stm, koh, emptying,
Nearest to new: york, zealand, papua, orleans, caledonia, jersey, testament, radically,
Nearest to as: well, such, referred, known, regarded, result, taft, unintelligent,
Nearest to would: otherwise, be, announced, seem, later, hoped, likely, become,
Nearest to frac: right, cdot, sin, dt, schwarzschild, left, zam, mathrm,
Nearest to defense: vultures, responsibility, defence, pannonia, aide, employed, kune, piety,
Nearest to construction: projects, elimination, gloucestershire, frobisher, cellphone, facility, materials, ilyich,
Nearest to engine: petrol, engines, combustion, automobile, airframe, chassis, cordobas, regents,
Nearest to consists: magistrate, consisting, consist, composed, extensive, cour, score, nacional,
Nearest to existence: personages, irrational, lorry, provability, tilapia, ant, tagus, baptise,
Nearest to troops: sanatorium, freed, azores, sarin, clad, glanville, feeder, frenchmen,
Nearest to egypt: egyptian, ancient, promised, postscript, cataphract, height, marchibroda, walkways,
Epoch 5/10 Iteration: 55100 Avg. Training loss: 3.9874 0.1652 sec/batch
Epoch 5/10 Iteration: 55200 Avg. Training loss: 3.9674 0.1597 sec/batch
Epoch 5/10 Iteration: 55300 Avg. Training loss: 4.0094 0.1625 sec/batch
Epoch 5/10 Iteration: 55400 Avg. Training loss: 3.9783 0.1594 sec/batch
Epoch 5/10 Iteration: 55500 Avg. Training loss: 3.7389 0.1567 sec/batch
Epoch 5/10 Iteration: 55600 Avg. Training loss: 3.8728 0.1680 sec/batch
Epoch 5/10 Iteration: 55700 Avg. Training loss: 3.9426 0.1662 sec/batch
Epoch 5/10 Iteration: 55800 Avg. Training loss: 3.9954 0.1613 sec/batch
Epoch 5/10 Iteration: 55900 Avg. Training loss: 4.0386 0.1564 sec/batch
Epoch 5/10 Iteration: 56000 Avg. Training loss: 3.9970 0.1592 sec/batch
Nearest to however: overdue, sacco, taiko, dour, pdpa, participates, scholars, reviewers,
Nearest to no: longer, there, yes, unshielded, indication, consensus, cytokine, import,
Nearest to were: they, benchers, tailless, stolen, bodies, printed, these, taa,
Nearest to two: nov, equ, sheng, spender, bids, folic, biel, jafar,
Nearest to called: so, sometimes, investigates, kirkcaldy, often, affectionately, flaccus, emptying,
Nearest to new: york, zealand, papua, orleans, caledonia, jersey, testament, radically,
Nearest to as: well, such, known, referred, regarded, result, taft, verhoeven,
Nearest to would: otherwise, later, be, announced, moranis, become, seem, hoped,
Nearest to frac: right, cdot, sin, schwarzschild, dt, zam, prod, left,
Nearest to defense: vultures, responsibility, defence, pannonia, aide, employed, department, morecambe,
Nearest to construction: gloucestershire, projects, elimination, frobisher, cellphone, ilyich, roxbury, facility,
Nearest to engine: petrol, engines, combustion, airframe, automobile, chassis, steering, cordobas,
Nearest to consists: magistrate, consisting, consist, score, extensive, composed, cour, nacional,
Nearest to existence: lorry, personages, tilapia, ant, provability, tagus, irrational, baptise,
Nearest to troops: freed, sanatorium, azores, sarin, clad, glanville, feeder, sieur,
Nearest to egypt: egyptian, ancient, promised, postscript, height, cataphract, marchibroda, walkways,
Epoch 5/10 Iteration: 56100 Avg. Training loss: 4.0668 0.1605 sec/batch
Epoch 5/10 Iteration: 56200 Avg. Training loss: 4.0712 0.1505 sec/batch
Epoch 5/10 Iteration: 56300 Avg. Training loss: 4.0763 0.1495 sec/batch
Epoch 5/10 Iteration: 56400 Avg. Training loss: 4.0194 0.1529 sec/batch
Epoch 5/10 Iteration: 56500 Avg. Training loss: 3.8863 0.1571 sec/batch
Epoch 5/10 Iteration: 56600 Avg. Training loss: 3.9870 0.1574 sec/batch
Epoch 5/10 Iteration: 56700 Avg. Training loss: 4.0155 0.1645 sec/batch
Epoch 5/10 Iteration: 56800 Avg. Training loss: 3.9791 0.1584 sec/batch
Epoch 5/10 Iteration: 56900 Avg. Training loss: 3.9693 0.1517 sec/batch
Epoch 5/10 Iteration: 57000 Avg. Training loss: 4.0468 0.1508 sec/batch
Nearest to however: overdue, sacco, taiko, dour, participates, pdpa, scholars, moves,
Nearest to no: longer, there, yes, cytokine, unshielded, consensus, indication, import,
Nearest to were: they, benchers, tailless, stolen, bodies, printed, taa, symphonic,
Nearest to two: nov, folic, ergodic, equ, sheng, spender, bids, guarantees,
Nearest to called: so, sometimes, investigates, kirkcaldy, often, also, emptying, stm,
Nearest to new: york, zealand, papua, orleans, caledonia, jersey, testament, radically,
Nearest to as: well, such, known, referred, regarded, result, taft, verhoeven,
Nearest to would: otherwise, be, later, moranis, announced, hoped, seem, become,
Nearest to frac: right, cdot, sin, schwarzschild, dt, zam, left, prod,
Nearest to defense: vultures, defence, pannonia, responsibility, aide, employed, piety, kune,
Nearest to construction: elimination, projects, gloucestershire, frobisher, cellphone, ilyich, orgy, facility,
Nearest to engine: petrol, engines, combustion, airframe, steering, automobile, chassis, cordobas,
Nearest to consists: magistrate, consisting, extensive, consist, score, composed, nacional, spaces,
Nearest to existence: tilapia, personages, ant, lorry, provability, tagus, baptise, irrational,
Nearest to troops: sanatorium, freed, azores, clad, sarin, glanville, feeder, maghreb,
Nearest to egypt: egyptian, promised, ancient, cataphract, height, postscript, marchibroda, walkways,
Epoch 5/10 Iteration: 57100 Avg. Training loss: 3.9801 0.1531 sec/batch
Epoch 5/10 Iteration: 57200 Avg. Training loss: 3.9685 0.1620 sec/batch
Epoch 5/10 Iteration: 57300 Avg. Training loss: 3.7963 0.1564 sec/batch
Epoch 5/10 Iteration: 57400 Avg. Training loss: 3.9331 0.1605 sec/batch
Epoch 5/10 Iteration: 57500 Avg. Training loss: 3.9915 0.1545 sec/batch
Epoch 5/10 Iteration: 57600 Avg. Training loss: 3.8818 0.1547 sec/batch
Epoch 5/10 Iteration: 57700 Avg. Training loss: 3.9830 0.1595 sec/batch
Epoch 5/10 Iteration: 57800 Avg. Training loss: 3.9743 0.1606 sec/batch
Epoch 5/10 Iteration: 57900 Avg. Training loss: 3.9953 0.1551 sec/batch
Epoch 5/10 Iteration: 58000 Avg. Training loss: 3.9812 0.1560 sec/batch
Nearest to however: overdue, sacco, taiko, dour, participates, scholars, reviewers, moves,
Nearest to no: longer, there, yes, consensus, cytokine, unshielded, indication, cheapest,
Nearest to were: they, tailless, stolen, benchers, printed, bodies, taa, symphonic,
Nearest to two: nov, equ, folic, sheng, spender, lazzeri, guarantees, ergodic,
Nearest to called: so, sometimes, investigates, kirkcaldy, also, japh, often, spinach,
Nearest to new: york, zealand, papua, orleans, caledonia, jersey, testament, radically,
Nearest to as: well, such, known, referred, regarded, result, taft, verhoeven,
Nearest to would: otherwise, later, be, announced, moranis, hoped, seem, become,
Nearest to frac: right, cdot, sin, zam, dt, schwarzschild, provider, left,
Nearest to defense: vultures, defence, responsibility, pannonia, aide, employed, kune, healing,
Nearest to construction: elimination, projects, frobisher, gloucestershire, materials, skyscraper, building, roxbury,
Nearest to engine: engines, petrol, combustion, chassis, airframe, steering, automobile, cordobas,
Nearest to consists: magistrate, consisting, extensive, consist, composed, score, nacional, includes,
Nearest to existence: personages, tilapia, ant, lorry, provability, tagus, baptise, irrational,
Nearest to troops: sanatorium, freed, clad, sarin, glanville, azores, feeder, tehran,
Nearest to egypt: egyptian, promised, ancient, height, cataphract, bloodthirsty, walkways, marchibroda,
Epoch 5/10 Iteration: 58100 Avg. Training loss: 3.9817 0.1597 sec/batch
Epoch 5/10 Iteration: 58200 Avg. Training loss: 4.0253 0.1552 sec/batch
Epoch 5/10 Iteration: 58300 Avg. Training loss: 4.0445 0.1548 sec/batch
Epoch 6/10 Iteration: 58400 Avg. Training loss: 4.0356 0.1550 sec/batch
Epoch 6/10 Iteration: 58500 Avg. Training loss: 3.9433 0.1569 sec/batch
Epoch 6/10 Iteration: 58600 Avg. Training loss: 4.0299 0.1634 sec/batch
Epoch 6/10 Iteration: 58700 Avg. Training loss: 3.9487 0.1520 sec/batch
Epoch 6/10 Iteration: 58800 Avg. Training loss: 3.9915 0.1552 sec/batch
Epoch 6/10 Iteration: 58900 Avg. Training loss: 3.9788 0.1529 sec/batch
Epoch 6/10 Iteration: 59000 Avg. Training loss: 3.9467 0.1588 sec/batch
Nearest to however: overdue, sacco, taiko, dour, scholars, participates, pdpa, reviewers,
Nearest to no: longer, there, yes, consensus, cytokine, unshielded, indication, exists,
Nearest to were: they, benchers, bodies, stolen, tailless, these, printed, manuscripts,
Nearest to two: nov, equ, sheng, folic, ergodic, jafar, biel, spender,
Nearest to called: so, sometimes, investigates, kirkcaldy, also, often, flaccus, spinach,
Nearest to new: york, zealand, papua, orleans, caledonia, jersey, testament, pluralism,
Nearest to as: well, such, known, referred, regarded, result, taft, described,
Nearest to would: otherwise, be, later, announced, hoped, moranis, seem, become,
Nearest to frac: right, cdot, sin, dt, zam, schwarzschild, prod, provider,
Nearest to defense: vultures, responsibility, defence, aide, pannonia, kune, employed, tuan,
Nearest to construction: elimination, projects, gloucestershire, frobisher, skyscraper, building, roxbury, materials,
Nearest to engine: petrol, engines, combustion, chassis, steering, automobile, cordobas, airframe,
Nearest to consists: magistrate, consisting, extensive, consist, composed, score, lying, dactylic,
Nearest to existence: personages, provability, ant, baptise, lorry, irrational, tilapia, ontological,
Nearest to troops: sanatorium, freed, clad, sarin, glanville, feeder, azores, maghreb,
Nearest to egypt: egyptian, ancient, promised, height, cataphract, postscript, marchibroda, walkways,
Epoch 6/10 Iteration: 59100 Avg. Training loss: 3.9455 0.1576 sec/batch
Epoch 6/10 Iteration: 59200 Avg. Training loss: 3.9809 0.1540 sec/batch
Epoch 6/10 Iteration: 59300 Avg. Training loss: 4.0310 0.1587 sec/batch
Epoch 6/10 Iteration: 59400 Avg. Training loss: 3.9696 0.1556 sec/batch
Epoch 6/10 Iteration: 59500 Avg. Training loss: 3.9869 0.1513 sec/batch
Epoch 6/10 Iteration: 59600 Avg. Training loss: 4.0121 0.1585 sec/batch
Epoch 6/10 Iteration: 59700 Avg. Training loss: 4.0014 0.1543 sec/batch
Epoch 6/10 Iteration: 59800 Avg. Training loss: 4.0018 0.1520 sec/batch
Epoch 6/10 Iteration: 59900 Avg. Training loss: 3.9763 0.1555 sec/batch
Epoch 6/10 Iteration: 60000 Avg. Training loss: 3.8390 0.1533 sec/batch
Nearest to however: overdue, sacco, taiko, participates, pdpa, dogger, scholars, dour,
Nearest to no: longer, there, yes, cytokine, consensus, unshielded, indication, exists,
Nearest to were: they, bodies, stolen, benchers, these, tailless, printed, taa,
Nearest to two: nov, zero, equ, folic, sheng, jafar, notified, ergodic,
Nearest to called: so, sometimes, investigates, often, spinach, also, kirkcaldy, flaccus,
Nearest to new: york, zealand, papua, orleans, caledonia, jersey, testament, radically,
Nearest to as: well, such, known, referred, regarded, result, taft, described,
Nearest to would: otherwise, be, announced, later, hoped, moranis, seem, fluency,
Nearest to frac: right, cdot, sin, dt, zam, schwarzschild, prod, provider,
Nearest to defense: vultures, defence, responsibility, pannonia, employed, aide, kune, forces,
Nearest to construction: projects, elimination, frobisher, materials, skyscraper, building, cellphone, gloucestershire,
Nearest to engine: engines, petrol, combustion, chassis, steering, automobile, airframe, cordobas,
Nearest to consists: consisting, magistrate, composed, extensive, consist, lying, of, nacional,
Nearest to existence: personages, tilapia, ant, provability, lorry, shimura, irrational, baptise,
Nearest to troops: sanatorium, freed, clad, sarin, feeder, glanville, azores, uppercamelcase,
Nearest to egypt: egyptian, ancient, promised, height, cataphract, postscript, bloodthirsty, marchibroda,
Epoch 6/10 Iteration: 60100 Avg. Training loss: 4.0441 0.1545 sec/batch
Epoch 6/10 Iteration: 60200 Avg. Training loss: 3.9965 0.1519 sec/batch
Epoch 6/10 Iteration: 60300 Avg. Training loss: 4.0274 0.1522 sec/batch
Epoch 6/10 Iteration: 60400 Avg. Training loss: 4.0165 0.1554 sec/batch
Epoch 6/10 Iteration: 60500 Avg. Training loss: 4.0078 0.1538 sec/batch
Epoch 6/10 Iteration: 60600 Avg. Training loss: 4.0059 0.1528 sec/batch
Epoch 6/10 Iteration: 60700 Avg. Training loss: 4.0304 0.1520 sec/batch
Epoch 6/10 Iteration: 60800 Avg. Training loss: 4.0292 0.1521 sec/batch
Epoch 6/10 Iteration: 60900 Avg. Training loss: 4.0091 0.1585 sec/batch
Epoch 6/10 Iteration: 61000 Avg. Training loss: 3.9356 0.1564 sec/batch
Nearest to however: overdue, sacco, taiko, participates, pdpa, scholars, dour, caucasians,
Nearest to no: longer, there, yes, consensus, cytokine, indication, unshielded, exists,
Nearest to were: they, bodies, tailless, stolen, benchers, these, printed, sensational,
Nearest to two: nov, equ, sheng, biel, ergodic, notified, nd, folic,
Nearest to called: so, sometimes, investigates, also, often, spinach, kirkcaldy, stm,
Nearest to new: york, zealand, papua, orleans, caledonia, jersey, testament, radically,
Nearest to as: well, such, known, referred, regarded, result, taft, described,
Nearest to would: otherwise, be, announced, later, hoped, moranis, seem, become,
Nearest to frac: right, cdot, sin, left, dt, prod, zam, schwarzschild,
Nearest to defense: vultures, employed, pannonia, defence, responsibility, kune, aide, forces,
Nearest to construction: projects, elimination, frobisher, building, gloucestershire, skyscraper, cellphone, materials,
Nearest to engine: engines, petrol, combustion, chassis, airframe, cordobas, steering, automobile,
Nearest to consists: consisting, consist, magistrate, composed, extensive, lying, score, of,
Nearest to existence: personages, tilapia, ant, lorry, shimura, irrational, provability, tagus,
Nearest to troops: sanatorium, clad, freed, sarin, feeder, azores, army, advance,
Nearest to egypt: egyptian, ancient, promised, height, cataphract, postscript, bloodthirsty, marchibroda,
Epoch 6/10 Iteration: 61100 Avg. Training loss: 3.9050 0.1649 sec/batch
Epoch 6/10 Iteration: 61200 Avg. Training loss: 3.8254 0.1512 sec/batch
Epoch 6/10 Iteration: 61300 Avg. Training loss: 3.9814 0.1504 sec/batch
Epoch 6/10 Iteration: 61400 Avg. Training loss: 3.9418 0.1503 sec/batch
Epoch 6/10 Iteration: 61500 Avg. Training loss: 3.9750 0.1506 sec/batch
Epoch 6/10 Iteration: 61600 Avg. Training loss: 3.9657 0.1532 sec/batch
Epoch 6/10 Iteration: 61700 Avg. Training loss: 3.9609 0.1508 sec/batch
Epoch 6/10 Iteration: 61800 Avg. Training loss: 3.8521 0.1502 sec/batch
Epoch 6/10 Iteration: 61900 Avg. Training loss: 3.9854 0.1504 sec/batch
Epoch 6/10 Iteration: 62000 Avg. Training loss: 3.9968 0.1502 sec/batch
Nearest to however: overdue, taiko, participates, sacco, pdpa, dour, scholars, goiter,
Nearest to no: longer, there, yes, consensus, cytokine, indication, exists, unshielded,
Nearest to were: they, stolen, tailless, bodies, printed, benchers, sensational, originally,
Nearest to two: nov, equ, nd, folic, sheng, biel, ergodic, notified,
Nearest to called: so, sometimes, investigates, also, often, kirkcaldy, spinach, affectionately,
Nearest to new: york, zealand, papua, orleans, caledonia, jersey, testament, radically,
Nearest to as: well, such, known, referred, regarded, result, taft, described,
Nearest to would: otherwise, be, announced, later, hoped, seem, moranis, likely,
Nearest to frac: right, cdot, sin, dt, left, prod, schwarzschild, zam,
Nearest to defense: vultures, employed, kune, pannonia, defence, responsibility, aide, healing,
Nearest to construction: projects, elimination, skyscraper, frobisher, building, materials, cellphone, gloucestershire,
Nearest to engine: engines, petrol, combustion, airframe, chassis, cordobas, steering, automobile,
Nearest to consists: consisting, consist, composed, magistrate, extensive, score, lying, includes,
Nearest to existence: personages, provability, tilapia, shimura, ant, ontological, irrational, qualitatively,
Nearest to troops: sanatorium, clad, freed, sarin, uppercamelcase, feeder, army, glanville,
Nearest to egypt: egyptian, ancient, promised, height, bloodthirsty, cataphract, pyramids, walkways,
Epoch 6/10 Iteration: 62100 Avg. Training loss: 3.9667 0.1555 sec/batch
Epoch 6/10 Iteration: 62200 Avg. Training loss: 4.0031 0.1502 sec/batch
Epoch 6/10 Iteration: 62300 Avg. Training loss: 3.9774 0.1512 sec/batch
Epoch 6/10 Iteration: 62400 Avg. Training loss: 3.9851 0.1506 sec/batch
Epoch 6/10 Iteration: 62500 Avg. Training loss: 4.0271 0.1527 sec/batch
Epoch 6/10 Iteration: 62600 Avg. Training loss: 3.9357 0.1567 sec/batch
Epoch 6/10 Iteration: 62700 Avg. Training loss: 3.9946 0.1532 sec/batch
Epoch 6/10 Iteration: 62800 Avg. Training loss: 3.9169 0.1526 sec/batch
Epoch 6/10 Iteration: 62900 Avg. Training loss: 4.0086 0.1558 sec/batch
Epoch 6/10 Iteration: 63000 Avg. Training loss: 4.0147 0.1567 sec/batch
Nearest to however: overdue, participates, sacco, taiko, dour, reviewers, pdpa, scholars,
Nearest to no: longer, there, yes, consensus, cytokine, indication, unshielded, exists,
Nearest to were: they, tailless, bodies, stolen, printed, originally, nuit, symphonic,
Nearest to two: nov, nd, folic, sheng, ergodic, equ, biel, spender,
Nearest to called: so, sometimes, investigates, also, often, kirkcaldy, stm, affectionately,
Nearest to new: york, zealand, papua, orleans, caledonia, jersey, testament, radically,
Nearest to as: well, such, known, referred, regarded, result, taft, described,
Nearest to would: otherwise, announced, be, later, hoped, seem, moranis, become,
Nearest to frac: right, cdot, sin, left, dt, schwarzschild, zam, provider,
Nearest to defense: vultures, defence, employed, responsibility, aide, pannonia, kune, zoff,
Nearest to construction: projects, elimination, skyscraper, materials, frobisher, building, cellphone, orgy,
Nearest to engine: engines, petrol, combustion, airframe, chassis, steering, cordobas, cycle,
Nearest to consists: consisting, consist, extensive, magistrate, composed, score, lying, dactylic,
Nearest to existence: personages, provability, ant, shimura, tilapia, ontological, ue, irrational,
Nearest to troops: sanatorium, clad, freed, glanville, uppercamelcase, feeder, sarin, azores,
Nearest to egypt: egyptian, ancient, promised, height, bloodthirsty, marchibroda, pyramids, cataphract,
Epoch 6/10 Iteration: 63100 Avg. Training loss: 4.0096 0.1582 sec/batch
Epoch 6/10 Iteration: 63200 Avg. Training loss: 4.0186 0.1585 sec/batch
Epoch 6/10 Iteration: 63300 Avg. Training loss: 3.9819 0.1511 sec/batch
Epoch 6/10 Iteration: 63400 Avg. Training loss: 3.8976 0.1520 sec/batch
Epoch 6/10 Iteration: 63500 Avg. Training loss: 3.9999 0.1513 sec/batch
Epoch 6/10 Iteration: 63600 Avg. Training loss: 3.9241 0.1513 sec/batch
Epoch 6/10 Iteration: 63700 Avg. Training loss: 4.0228 0.1511 sec/batch
Epoch 6/10 Iteration: 63800 Avg. Training loss: 3.9891 0.1516 sec/batch
Epoch 6/10 Iteration: 63900 Avg. Training loss: 3.9673 0.1512 sec/batch
Epoch 6/10 Iteration: 64000 Avg. Training loss: 4.0178 0.1515 sec/batch
Nearest to however: overdue, taiko, sacco, pdpa, participates, dour, scholars, reviewers,
Nearest to no: longer, there, yes, consensus, indication, keck, exists, princess,
Nearest to were: they, bodies, tailless, stolen, nuit, benchers, originally, these,
Nearest to two: folic, nov, sheng, ergodic, nd, lactantius, guarantees, equ,
Nearest to called: so, sometimes, investigates, also, kirkcaldy, often, koh, spinach,
Nearest to new: york, zealand, papua, orleans, caledonia, jersey, testament, pluralism,
Nearest to as: well, such, known, referred, regarded, result, taft, described,
Nearest to would: otherwise, later, be, announced, seem, moranis, hoped, become,
Nearest to frac: right, cdot, sin, left, dt, zam, schwarzschild, provider,
Nearest to defense: vultures, defence, responsibility, pannonia, aide, employed, healing, kune,
Nearest to construction: projects, elimination, frobisher, skyscraper, cellphone, materials, building, expressway,
Nearest to engine: engines, petrol, combustion, airframe, chassis, bcs, steering, cycle,
Nearest to consists: consisting, consist, magistrate, extensive, score, composed, lying, dactylic,
Nearest to existence: personages, tilapia, ant, shimura, provability, ue, tagus, irrational,
Nearest to troops: sanatorium, clad, freed, glanville, azores, frenchmen, uppercamelcase, sieur,
Nearest to egypt: egyptian, ancient, promised, height, postscript, walkways, pyramids, marchibroda,
Epoch 6/10 Iteration: 64100 Avg. Training loss: 3.9221 0.1570 sec/batch
Epoch 6/10 Iteration: 64200 Avg. Training loss: 3.9911 0.1539 sec/batch
Epoch 6/10 Iteration: 64300 Avg. Training loss: 3.9237 0.1551 sec/batch
Epoch 6/10 Iteration: 64400 Avg. Training loss: 3.9888 0.1505 sec/batch
Epoch 6/10 Iteration: 64500 Avg. Training loss: 3.8727 0.1510 sec/batch
Epoch 6/10 Iteration: 64600 Avg. Training loss: 3.9703 0.1516 sec/batch
Epoch 6/10 Iteration: 64700 Avg. Training loss: 4.0129 0.1514 sec/batch
Epoch 6/10 Iteration: 64800 Avg. Training loss: 3.9426 0.1505 sec/batch
Epoch 6/10 Iteration: 64900 Avg. Training loss: 3.9596 0.1509 sec/batch
Epoch 6/10 Iteration: 65000 Avg. Training loss: 3.9248 0.1505 sec/batch
Nearest to however: overdue, taiko, pdpa, sacco, participates, dour, bedford, dogger,
Nearest to no: longer, there, import, yes, exists, info, unshielded, duplicate,
Nearest to were: they, bodies, these, benchers, tailless, nuit, stolen, originally,
Nearest to two: folic, zero, nov, sheng, guarantees, equ, jafar, ergodic,
Nearest to called: so, sometimes, investigates, kirkcaldy, often, also, japh, stm,
Nearest to new: york, zealand, papua, orleans, caledonia, jersey, testament, radically,
Nearest to as: well, such, known, referred, regarded, result, taft, described,
Nearest to would: otherwise, be, announced, later, seem, hoped, become, moranis,
Nearest to frac: right, cdot, sin, dt, schwarzschild, zam, left, provider,
Nearest to defense: vultures, responsibility, defence, aide, employed, pannonia, healing, seti,
Nearest to construction: projects, elimination, frobisher, materials, cellphone, skyscraper, building, scales,
Nearest to engine: engines, petrol, combustion, steering, chassis, cordobas, bcs, airframe,
Nearest to consists: consisting, consist, extensive, magistrate, composed, score, lying, includes,
Nearest to existence: personages, ant, provability, shimura, tilapia, ackermann, multitude, ue,
Nearest to troops: sanatorium, freed, clad, azores, glanville, frenchmen, sarin, army,
Nearest to egypt: egyptian, promised, ancient, height, postscript, walkways, marchibroda, cataphract,
Epoch 6/10 Iteration: 65100 Avg. Training loss: 3.9830 0.1533 sec/batch
Epoch 6/10 Iteration: 65200 Avg. Training loss: 4.0186 0.1510 sec/batch
Epoch 6/10 Iteration: 65300 Avg. Training loss: 3.9945 0.1516 sec/batch
Epoch 6/10 Iteration: 65400 Avg. Training loss: 4.0286 0.1591 sec/batch
Epoch 6/10 Iteration: 65500 Avg. Training loss: 4.0013 0.1580 sec/batch
Epoch 6/10 Iteration: 65600 Avg. Training loss: 3.9308 0.1631 sec/batch
Epoch 6/10 Iteration: 65700 Avg. Training loss: 4.0191 0.1550 sec/batch
Epoch 6/10 Iteration: 65800 Avg. Training loss: 4.0148 0.1519 sec/batch
Epoch 6/10 Iteration: 65900 Avg. Training loss: 3.9670 0.1573 sec/batch
Epoch 6/10 Iteration: 66000 Avg. Training loss: 4.0032 0.1532 sec/batch
Nearest to however: overdue, taiko, sacco, pdpa, participates, dour, hellbender, bedford,
Nearest to no: longer, there, import, yes, whatsoever, exists, cytokine, consensus,
Nearest to were: they, bodies, these, benchers, stolen, tailless, nuit, originally,
Nearest to two: folic, nov, sheng, spender, ergodic, bids, guarantees, lactantius,
Nearest to called: so, sometimes, investigates, often, also, kirkcaldy, spinach, emptying,
Nearest to new: york, zealand, papua, orleans, caledonia, jersey, testament, radically,
Nearest to as: well, such, known, referred, regarded, result, taft, unintelligent,
Nearest to would: otherwise, announced, later, be, seem, become, moranis, hoped,
Nearest to frac: right, cdot, dt, sin, zam, left, schwarzschild, prod,
Nearest to defense: vultures, pannonia, aide, responsibility, defence, employed, seti, zoff,
Nearest to construction: projects, elimination, frobisher, materials, skyscraper, bunch, cellphone, expressway,
Nearest to engine: engines, petrol, combustion, bcs, steering, chassis, cycle, search,
Nearest to consists: consisting, consist, extensive, magistrate, lying, composed, score, dactylic,
Nearest to existence: personages, ant, tilapia, shimura, provability, tagus, disks, qualitatively,
Nearest to troops: sanatorium, freed, clad, army, frenchmen, glanville, azores, maghreb,
Nearest to egypt: egyptian, ancient, promised, height, postscript, marchibroda, pyramids, cataphract,
Epoch 6/10 Iteration: 66100 Avg. Training loss: 4.0161 0.1577 sec/batch
Epoch 6/10 Iteration: 66200 Avg. Training loss: 3.9868 0.1537 sec/batch
Epoch 6/10 Iteration: 66300 Avg. Training loss: 3.9932 0.1510 sec/batch
Epoch 6/10 Iteration: 66400 Avg. Training loss: 3.9799 0.1528 sec/batch
Epoch 6/10 Iteration: 66500 Avg. Training loss: 3.9033 0.1561 sec/batch
Epoch 6/10 Iteration: 66600 Avg. Training loss: 3.9556 0.1578 sec/batch
Epoch 6/10 Iteration: 66700 Avg. Training loss: 3.9530 0.1582 sec/batch
Epoch 6/10 Iteration: 66800 Avg. Training loss: 3.9649 0.1540 sec/batch
Epoch 6/10 Iteration: 66900 Avg. Training loss: 3.9355 0.1536 sec/batch
Epoch 6/10 Iteration: 67000 Avg. Training loss: 4.0471 0.1548 sec/batch
Nearest to however: taiko, overdue, sacco, pdpa, participates, hellbender, asylum, dour,
Nearest to no: longer, there, import, yes, whatsoever, consensus, exists, cytokine,
Nearest to were: they, bodies, benchers, these, tailless, stolen, nuit, originally,
Nearest to two: nov, folic, jafar, nd, zero, equ, bids, sheng,
Nearest to called: so, sometimes, investigates, often, also, stm, japh, spinach,
Nearest to new: york, zealand, papua, caledonia, orleans, jersey, testament, radically,
Nearest to as: well, such, known, referred, regarded, result, taft, unintelligent,
Nearest to would: otherwise, later, announced, be, seem, become, hoped, likely,
Nearest to frac: right, cdot, zam, dt, sin, schwarzschild, left, provider,
Nearest to defense: vultures, aide, defence, pannonia, employed, responsibility, healing, braveheart,
Nearest to construction: projects, elimination, frobisher, expressway, cellphone, materials, skyscraper, intruder,
Nearest to engine: engines, petrol, combustion, bcs, steering, chassis, automobile, cycle,
Nearest to consists: consisting, consist, composed, extensive, magistrate, lying, score, includes,
Nearest to existence: personages, provability, ant, tilapia, shimura, irrational, tagus, ontological,
Nearest to troops: sanatorium, freed, clad, frenchmen, glanville, azores, rebuff, feeder,
Nearest to egypt: egyptian, ancient, promised, height, postscript, pyramids, cataphract, walkways,
Epoch 6/10 Iteration: 67100 Avg. Training loss: 3.8250 0.1632 sec/batch
Epoch 6/10 Iteration: 67200 Avg. Training loss: 3.8126 0.1584 sec/batch
Epoch 6/10 Iteration: 67300 Avg. Training loss: 3.8429 0.1607 sec/batch
Epoch 6/10 Iteration: 67400 Avg. Training loss: 3.9276 0.1642 sec/batch
Epoch 6/10 Iteration: 67500 Avg. Training loss: 4.0238 0.1620 sec/batch
Epoch 6/10 Iteration: 67600 Avg. Training loss: 4.0541 0.1667 sec/batch
Epoch 6/10 Iteration: 67700 Avg. Training loss: 3.9811 0.1676 sec/batch
Epoch 6/10 Iteration: 67800 Avg. Training loss: 4.0670 0.1647 sec/batch
Epoch 6/10 Iteration: 67900 Avg. Training loss: 4.0596 0.1659 sec/batch
Epoch 6/10 Iteration: 68000 Avg. Training loss: 4.0313 0.1662 sec/batch
Nearest to however: taiko, overdue, sacco, pdpa, dour, hellbender, moves, toppled,
Nearest to no: longer, there, yes, import, whatsoever, unshielded, consensus, symphony,
Nearest to were: they, bodies, benchers, these, tailless, stolen, nuit, taa,
Nearest to two: nov, nd, folic, bids, sheng, lactantius, jafar, spender,
Nearest to called: so, sometimes, investigates, often, also, spinach, kirkcaldy, emptying,
Nearest to new: york, zealand, papua, orleans, caledonia, testament, jersey, radically,
Nearest to as: well, such, known, referred, regarded, result, taft, unintelligent,
Nearest to would: otherwise, announced, be, later, become, seem, moranis, hoped,
Nearest to frac: right, cdot, sin, dt, zam, schwarzschild, left, prod,
Nearest to defense: vultures, aide, employed, pannonia, defence, healing, responsibility, braveheart,
Nearest to construction: projects, elimination, frobisher, skyscraper, expressway, ilyich, gloucestershire, canal,
Nearest to engine: engines, petrol, combustion, steering, bcs, chassis, cordobas, automobile,
Nearest to consists: consisting, consist, composed, magistrate, extensive, lying, score, includes,
Nearest to existence: personages, tilapia, ant, provability, tagus, shimura, lorry, irrational,
Nearest to troops: sanatorium, clad, freed, glanville, azores, frenchmen, rebuff, feeder,
Nearest to egypt: egyptian, ancient, height, promised, postscript, pyramids, bloodthirsty, marchibroda,
Epoch 6/10 Iteration: 68100 Avg. Training loss: 3.9923 0.1724 sec/batch
Epoch 6/10 Iteration: 68200 Avg. Training loss: 3.8428 0.1739 sec/batch
Epoch 6/10 Iteration: 68300 Avg. Training loss: 3.9899 0.1681 sec/batch
Epoch 6/10 Iteration: 68400 Avg. Training loss: 3.9676 0.1725 sec/batch
Epoch 6/10 Iteration: 68500 Avg. Training loss: 4.0027 0.1734 sec/batch
Epoch 6/10 Iteration: 68600 Avg. Training loss: 3.9271 0.1859 sec/batch
Epoch 6/10 Iteration: 68700 Avg. Training loss: 4.0222 0.1839 sec/batch
Epoch 6/10 Iteration: 68800 Avg. Training loss: 3.9560 0.1837 sec/batch
Epoch 6/10 Iteration: 68900 Avg. Training loss: 3.8981 0.1901 sec/batch
Epoch 6/10 Iteration: 69000 Avg. Training loss: 3.7984 0.1931 sec/batch
Nearest to however: taiko, sacco, overdue, pdpa, hellbender, dour, participates, moves,
Nearest to no: longer, there, yes, consensus, whatsoever, import, exists, unshielded,
Nearest to were: they, bodies, stolen, benchers, these, tailless, symphonic, taa,
Nearest to two: zero, nov, folic, nd, guarantees, thirds, railroaders, sheng,
Nearest to called: so, sometimes, investigates, spinach, also, often, emptying, kirkcaldy,
Nearest to new: york, zealand, papua, orleans, caledonia, jersey, testament, radically,
Nearest to as: well, such, known, referred, result, regarded, taft, described,
Nearest to would: otherwise, later, be, announced, become, hoped, moranis, seem,
Nearest to frac: right, cdot, sin, left, zam, dt, schwarzschild, provider,
Nearest to defense: vultures, defence, aide, pannonia, employed, responsibility, healing, forces,
Nearest to construction: projects, elimination, frobisher, expressway, materials, skyscraper, bunch, canal,
Nearest to engine: engines, petrol, combustion, bcs, cordobas, automobile, chassis, steering,
Nearest to consists: consisting, consist, magistrate, composed, extensive, of, includes, score,
Nearest to existence: personages, provability, tilapia, ant, tagus, shimura, ontological, ackermann,
Nearest to troops: sanatorium, clad, freed, rebuff, azores, glanville, maghreb, uppercamelcase,
Nearest to egypt: egyptian, ancient, height, promised, pyramids, postscript, nubia, cataphract,
Epoch 6/10 Iteration: 69100 Avg. Training loss: 3.9908 0.1899 sec/batch
Epoch 6/10 Iteration: 69200 Avg. Training loss: 3.9438 0.1849 sec/batch
Epoch 6/10 Iteration: 69300 Avg. Training loss: 3.9213 0.1839 sec/batch
Epoch 6/10 Iteration: 69400 Avg. Training loss: 3.9843 0.2007 sec/batch
Epoch 6/10 Iteration: 69500 Avg. Training loss: 3.9750 0.1758 sec/batch
Epoch 6/10 Iteration: 69600 Avg. Training loss: 3.9333 0.1610 sec/batch
Epoch 6/10 Iteration: 69700 Avg. Training loss: 3.9625 0.1586 sec/batch
Epoch 6/10 Iteration: 69800 Avg. Training loss: 3.9832 0.1630 sec/batch
Epoch 6/10 Iteration: 69900 Avg. Training loss: 4.0365 0.1617 sec/batch
Epoch 7/10 Iteration: 70000 Avg. Training loss: 4.0057 0.0637 sec/batch
Nearest to however: taiko, sacco, overdue, moves, pdpa, dour, participates, toppled,
Nearest to no: longer, there, whatsoever, yes, consensus, unshielded, exists, import,
Nearest to were: they, stolen, bodies, these, tailless, symphonic, taa, benchers,
Nearest to two: nov, folic, wrestlemania, sheng, nd, equ, lazzeri, ergodic,
Nearest to called: so, sometimes, investigates, often, also, spinach, japh, kirkcaldy,
Nearest to new: york, zealand, papua, orleans, caledonia, jersey, testament, radically,
Nearest to as: well, such, known, referred, result, regarded, taft, described,
Nearest to would: otherwise, be, later, announced, moranis, become, hoped, seem,
Nearest to frac: right, cdot, zam, left, dt, sin, schwarzschild, bodhisattvas,
Nearest to defense: vultures, defence, aide, pannonia, employed, responsibility, healing, braveheart,
Nearest to construction: projects, elimination, expressway, skyscraper, frobisher, intruder, canal, bunch,
Nearest to engine: engines, petrol, combustion, airframe, cordobas, bcs, chassis, automobile,
Nearest to consists: consisting, consist, magistrate, extensive, composed, score, includes, of,
Nearest to existence: personages, provability, ant, parallel, tilapia, tagus, shimura, ackermann,
Nearest to troops: sanatorium, clad, freed, glanville, feeder, rebuff, frenchmen, maghreb,
Nearest to egypt: egyptian, ancient, promised, height, postscript, pyramids, bloodthirsty, marchibroda,
Epoch 7/10 Iteration: 70100 Avg. Training loss: 3.9837 0.1815 sec/batch
Epoch 7/10 Iteration: 70200 Avg. Training loss: 3.9393 0.1765 sec/batch
Epoch 7/10 Iteration: 70300 Avg. Training loss: 4.0041 0.1752 sec/batch
Epoch 7/10 Iteration: 70400 Avg. Training loss: 3.9541 0.1771 sec/batch
Epoch 7/10 Iteration: 70500 Avg. Training loss: 3.9675 0.1811 sec/batch
Epoch 7/10 Iteration: 70600 Avg. Training loss: 3.9514 0.1801 sec/batch
Epoch 7/10 Iteration: 70700 Avg. Training loss: 3.9101 0.1733 sec/batch
Epoch 7/10 Iteration: 70800 Avg. Training loss: 3.9761 0.1732 sec/batch
Epoch 7/10 Iteration: 70900 Avg. Training loss: 3.9941 0.1736 sec/batch
Epoch 7/10 Iteration: 71000 Avg. Training loss: 3.9821 0.1742 sec/batch
Nearest to however: sacco, taiko, overdue, pdpa, dour, toppled, hellbender, scholars,
Nearest to no: longer, there, whatsoever, yes, exists, consensus, evidence, unshielded,
Nearest to were: they, stolen, bodies, these, originally, tailless, printed, frankfurter,
Nearest to two: nov, nd, equ, folic, jafar, sheng, ergodic, biel,
Nearest to called: so, sometimes, investigates, also, often, spinach, kirkcaldy, oriente,
Nearest to new: york, zealand, papua, orleans, caledonia, testament, jersey, radically,
Nearest to as: well, such, known, referred, regarded, result, taft, described,
Nearest to would: otherwise, be, announced, later, hoped, seem, become, moranis,
Nearest to frac: right, cdot, zam, dt, sin, left, schwarzschild, bodhisattvas,
Nearest to defense: vultures, defence, healing, aide, pannonia, responsibility, employed, zoff,
Nearest to construction: projects, elimination, expressway, skyscraper, frobisher, canal, bunch, gloucestershire,
Nearest to engine: engines, petrol, combustion, bcs, automobile, chassis, cordobas, steering,
Nearest to consists: consisting, consist, composed, magistrate, extensive, score, serres, includes,
Nearest to existence: personages, provability, ontological, shimura, ant, tilapia, baptise, irrational,
Nearest to troops: sanatorium, clad, freed, glanville, feeder, rebuff, sieur, uppercamelcase,
Nearest to egypt: egyptian, ancient, promised, height, pyramids, postscript, bloodthirsty, cataphract,
Epoch 7/10 Iteration: 71100 Avg. Training loss: 3.9602 0.1751 sec/batch
Epoch 7/10 Iteration: 71200 Avg. Training loss: 3.9873 0.1706 sec/batch
Epoch 7/10 Iteration: 71300 Avg. Training loss: 3.9773 0.1700 sec/batch
Epoch 7/10 Iteration: 71400 Avg. Training loss: 3.9894 0.1730 sec/batch
Epoch 7/10 Iteration: 71500 Avg. Training loss: 3.9653 0.1736 sec/batch
Epoch 7/10 Iteration: 71600 Avg. Training loss: 3.9613 0.1711 sec/batch
Epoch 7/10 Iteration: 71700 Avg. Training loss: 3.8402 0.1705 sec/batch
Epoch 7/10 Iteration: 71800 Avg. Training loss: 3.9985 0.1706 sec/batch
Epoch 7/10 Iteration: 71900 Avg. Training loss: 4.0248 0.1776 sec/batch
Epoch 7/10 Iteration: 72000 Avg. Training loss: 3.9973 0.1721 sec/batch
Nearest to however: taiko, sacco, overdue, pdpa, hellbender, participates, reviewers, dogger,
Nearest to no: longer, there, yes, whatsoever, consensus, exists, unshielded, cytokine,
Nearest to were: they, stolen, these, originally, bantu, bodies, tailless, printed,
Nearest to two: nov, nd, jafar, sheng, folic, ergodic, lactantius, biel,
Nearest to called: so, sometimes, investigates, spinach, often, also, kirkcaldy, affectionately,
Nearest to new: york, zealand, papua, orleans, caledonia, testament, jersey, radically,
Nearest to as: well, such, known, referred, result, regarded, taft, described,
Nearest to would: otherwise, be, announced, hoped, later, become, moranis, seem,
Nearest to frac: right, cdot, zam, dt, sin, schwarzschild, left, sqrt,
Nearest to defense: vultures, defence, pannonia, aide, healing, employed, responsibility, forces,
Nearest to construction: projects, elimination, expressway, skyscraper, bunch, facility, frobisher, canal,
Nearest to engine: engines, combustion, petrol, automobile, chassis, bcs, airframe, cordobas,
Nearest to consists: consisting, consist, composed, extensive, of, magistrate, includes, score,
Nearest to existence: personages, provability, shimura, ontological, tilapia, ant, brummell, ackermann,
Nearest to troops: sanatorium, clad, freed, glanville, feeder, uppercamelcase, rebuff, frenchmen,
Nearest to egypt: egyptian, ancient, promised, height, pyramids, bloodthirsty, cataphract, postscript,
Epoch 7/10 Iteration: 72100 Avg. Training loss: 4.0142 0.1748 sec/batch
Epoch 7/10 Iteration: 72200 Avg. Training loss: 4.0115 0.1705 sec/batch
Epoch 7/10 Iteration: 72300 Avg. Training loss: 3.9837 0.1743 sec/batch
Epoch 7/10 Iteration: 72400 Avg. Training loss: 3.9956 0.1725 sec/batch
Epoch 7/10 Iteration: 72500 Avg. Training loss: 4.0176 0.1736 sec/batch
Epoch 7/10 Iteration: 72600 Avg. Training loss: 3.9521 0.1745 sec/batch
Epoch 7/10 Iteration: 72700 Avg. Training loss: 3.9414 0.1692 sec/batch
Epoch 7/10 Iteration: 72800 Avg. Training loss: 3.8410 0.1664 sec/batch
Epoch 7/10 Iteration: 72900 Avg. Training loss: 3.8689 0.1643 sec/batch
Epoch 7/10 Iteration: 73000 Avg. Training loss: 3.9899 0.1706 sec/batch
Nearest to however: taiko, sacco, participates, overdue, hellbender, caucasians, pdpa, scholars,
Nearest to no: longer, there, yes, whatsoever, consensus, exists, evidence, unshielded,
Nearest to were: they, stolen, bantu, bodies, originally, these, tailless, printed,
Nearest to two: zero, nov, nd, folic, sheng, jafar, equ, ergodic,
Nearest to called: so, sometimes, investigates, spinach, often, also, oriente, kirkcaldy,
Nearest to new: york, zealand, papua, caledonia, orleans, testament, jersey, radically,
Nearest to as: well, such, known, referred, result, regarded, taft, described,
Nearest to would: otherwise, be, announced, hoped, become, later, moranis, seem,
Nearest to frac: right, cdot, zam, dt, left, sqrt, schwarzschild, sin,
Nearest to defense: vultures, defence, pannonia, aide, healing, employed, responsibility, forces,
Nearest to construction: projects, expressway, elimination, skyscraper, bunch, frobisher, building, materials,
Nearest to engine: engines, petrol, combustion, bcs, chassis, automobile, airframe, cordobas,
Nearest to consists: consisting, consist, composed, of, extensive, includes, magistrate, serres,
Nearest to existence: provability, personages, tilapia, shimura, ontological, concede, ant, anecdotal,
Nearest to troops: clad, sanatorium, freed, rebuff, glanville, feeder, frenchmen, uppercamelcase,
Nearest to egypt: egyptian, ancient, promised, height, pyramids, bloodthirsty, cataphract, nubia,
Epoch 7/10 Iteration: 73100 Avg. Training loss: 3.9287 0.1801 sec/batch
Epoch 7/10 Iteration: 73200 Avg. Training loss: 3.9462 0.1753 sec/batch
Epoch 7/10 Iteration: 73300 Avg. Training loss: 3.9490 0.1771 sec/batch
Epoch 7/10 Iteration: 73400 Avg. Training loss: 3.9327 0.1801 sec/batch
Epoch 7/10 Iteration: 73500 Avg. Training loss: 3.8583 0.1756 sec/batch
Epoch 7/10 Iteration: 73600 Avg. Training loss: 3.9675 0.1772 sec/batch
Epoch 7/10 Iteration: 73700 Avg. Training loss: 3.9809 0.1769 sec/batch
Epoch 7/10 Iteration: 73800 Avg. Training loss: 3.9690 0.1801 sec/batch
Epoch 7/10 Iteration: 73900 Avg. Training loss: 3.9737 0.1778 sec/batch
Epoch 7/10 Iteration: 74000 Avg. Training loss: 3.9437 0.1635 sec/batch
Nearest to however: taiko, sacco, participates, overdue, hellbender, bedford, noted, unrecognizable,
Nearest to no: longer, there, yes, whatsoever, exists, consensus, evidence, unshielded,
Nearest to were: they, stolen, originally, tailless, bodies, these, bantu, printed,
Nearest to two: nd, nov, ergodic, sheng, lactantius, folic, bids, biel,
Nearest to called: so, sometimes, investigates, often, also, spinach, oriente, kirkcaldy,
Nearest to new: york, zealand, papua, orleans, caledonia, testament, jersey, radically,
Nearest to as: well, such, known, referred, regarded, result, taft, described,
Nearest to would: otherwise, be, announced, hoped, become, later, seem, moranis,
Nearest to frac: right, cdot, dt, left, zam, schwarzschild, sin, tan,
Nearest to defense: vultures, defence, healing, employed, pannonia, aide, dov, zoff,
Nearest to construction: projects, elimination, skyscraper, expressway, building, bunch, orgy, materials,
Nearest to engine: petrol, combustion, engines, bcs, cooled, cordobas, steering, airframe,
Nearest to consists: consist, consisting, composed, extensive, of, includes, magistrate, serres,
Nearest to existence: personages, provability, ontological, tilapia, shimura, concede, ant, godhead,
Nearest to troops: clad, sanatorium, freed, rebuff, feeder, needles, knobs, glanville,
Nearest to egypt: egyptian, ancient, height, promised, pyramids, bloodthirsty, postscript, pharaoh,
Epoch 7/10 Iteration: 74100 Avg. Training loss: 3.9938 0.1596 sec/batch
Epoch 7/10 Iteration: 74200 Avg. Training loss: 3.9916 0.1568 sec/batch
Epoch 7/10 Iteration: 74300 Avg. Training loss: 3.9248 0.1608 sec/batch
Epoch 7/10 Iteration: 74400 Avg. Training loss: 3.9623 0.1772 sec/batch
Epoch 7/10 Iteration: 74500 Avg. Training loss: 3.9385 0.1771 sec/batch
Epoch 7/10 Iteration: 74600 Avg. Training loss: 3.9872 0.1809 sec/batch
Epoch 7/10 Iteration: 74700 Avg. Training loss: 4.0084 0.2103 sec/batch
Epoch 7/10 Iteration: 74800 Avg. Training loss: 4.0104 0.1729 sec/batch
Epoch 7/10 Iteration: 74900 Avg. Training loss: 3.9998 0.1600 sec/batch
Epoch 7/10 Iteration: 75000 Avg. Training loss: 3.9356 0.1593 sec/batch
Nearest to however: taiko, sacco, participates, overdue, hellbender, scholars, noted, pdpa,
Nearest to no: longer, there, yes, whatsoever, consensus, exists, unshielded, evidence,
Nearest to were: they, stolen, originally, tailless, bodies, bantu, these, nuit,
Nearest to two: nd, nov, sheng, ergodic, bids, biel, lactantius, folic,
Nearest to called: so, sometimes, investigates, often, also, japh, kirkcaldy, spinach,
Nearest to new: york, zealand, papua, orleans, caledonia, testament, jersey, radically,
Nearest to as: well, such, known, referred, regarded, result, taft, described,
Nearest to would: otherwise, announced, be, hoped, later, seem, become, moranis,
Nearest to frac: right, cdot, dt, zam, left, schwarzschild, sin, newton,
Nearest to defense: vultures, defence, healing, employed, pannonia, aide, braveheart, responsibility,
Nearest to construction: projects, skyscraper, expressway, elimination, bunch, frobisher, orgy, scales,
Nearest to engine: petrol, combustion, engines, bcs, cooled, cordobas, steering, airframe,
Nearest to consists: consist, consisting, composed, extensive, serres, of, includes, magistrate,
Nearest to existence: personages, provability, ontological, tilapia, shimura, ant, concede, ackermann,
Nearest to troops: sanatorium, clad, freed, rebuff, feeder, glanville, frenchmen, advance,
Nearest to egypt: egyptian, ancient, promised, bloodthirsty, height, pyramids, pharaoh, sars,
Epoch 7/10 Iteration: 75100 Avg. Training loss: 3.9281 0.1627 sec/batch
Epoch 7/10 Iteration: 75200 Avg. Training loss: 3.9161 0.1574 sec/batch
Epoch 7/10 Iteration: 75300 Avg. Training loss: 3.9549 0.1679 sec/batch
Epoch 7/10 Iteration: 75400 Avg. Training loss: 3.9790 0.1782 sec/batch
Epoch 7/10 Iteration: 75500 Avg. Training loss: 3.9510 0.1782 sec/batch
Epoch 7/10 Iteration: 75600 Avg. Training loss: 3.9962 0.1781 sec/batch
Epoch 7/10 Iteration: 75700 Avg. Training loss: 3.9992 0.1770 sec/batch
Epoch 7/10 Iteration: 75800 Avg. Training loss: 3.9067 0.1626 sec/batch
Epoch 7/10 Iteration: 75900 Avg. Training loss: 3.9716 0.1595 sec/batch
Epoch 7/10 Iteration: 76000 Avg. Training loss: 3.9090 0.1565 sec/batch
Nearest to however: taiko, sacco, overdue, hellbender, participates, pdpa, reviewers, noted,
Nearest to no: longer, there, yes, whatsoever, consensus, exists, unshielded, symphony,
Nearest to were: they, originally, these, stolen, tailless, symphonic, nuit, bantu,
Nearest to two: nd, equ, nov, sheng, zero, folic, jafar, ergodic,
Nearest to called: so, sometimes, investigates, kirkcaldy, often, also, spinach, japh,
Nearest to new: york, zealand, papua, orleans, caledonia, testament, jersey, pluralism,
Nearest to as: well, such, referred, known, regarded, result, taft, described,
Nearest to would: otherwise, announced, be, hoped, later, become, seem, moranis,
Nearest to frac: right, cdot, left, dt, zam, schwarzschild, sin, newton,
Nearest to defense: vultures, defence, healing, employed, pannonia, aide, braveheart, dov,
Nearest to construction: projects, expressway, skyscraper, elimination, bunch, frobisher, orgy, building,
Nearest to engine: combustion, petrol, engines, bcs, automobile, cordobas, cycle, cooled,
Nearest to consists: consisting, consist, composed, extensive, includes, serres, magistrate, score,
Nearest to existence: personages, provability, tilapia, ontological, ant, disks, shimura, tagus,
Nearest to troops: sanatorium, clad, freed, rebuff, frenchmen, glanville, feeder, sieur,
Nearest to egypt: egyptian, ancient, pyramids, promised, height, postscript, assistance, bloodthirsty,
Epoch 7/10 Iteration: 76100 Avg. Training loss: 4.0212 0.1583 sec/batch
Epoch 7/10 Iteration: 76200 Avg. Training loss: 3.7991 0.1556 sec/batch
Epoch 7/10 Iteration: 76300 Avg. Training loss: 3.9822 0.1616 sec/batch
Epoch 7/10 Iteration: 76400 Avg. Training loss: 4.0110 0.1619 sec/batch
Epoch 7/10 Iteration: 76500 Avg. Training loss: 3.9260 0.1613 sec/batch
Epoch 7/10 Iteration: 76600 Avg. Training loss: 3.8604 0.1579 sec/batch
Epoch 7/10 Iteration: 76700 Avg. Training loss: 3.9850 0.1639 sec/batch
Epoch 7/10 Iteration: 76800 Avg. Training loss: 3.9947 0.1611 sec/batch
Epoch 7/10 Iteration: 76900 Avg. Training loss: 3.9740 0.1680 sec/batch
Epoch 7/10 Iteration: 77000 Avg. Training loss: 3.9768 0.1624 sec/batch
Nearest to however: taiko, overdue, sacco, hellbender, participates, reviewers, pdpa, noted,
Nearest to no: longer, there, import, whatsoever, exists, duplicate, info, yes,
Nearest to were: they, these, stolen, bantu, originally, nuit, bodies, tailless,
Nearest to two: nd, nov, zero, equ, folic, jafar, sheng, ergodic,
Nearest to called: so, sometimes, investigates, often, spinach, kirkcaldy, also, japh,
Nearest to new: york, zealand, papua, orleans, caledonia, testament, jersey, radically,
Nearest to as: well, such, known, referred, regarded, result, taft, described,
Nearest to would: otherwise, announced, be, hoped, later, seem, become, moranis,
Nearest to frac: right, cdot, left, zam, dt, schwarzschild, sin, bodhisattvas,
Nearest to defense: vultures, defence, healing, employed, pannonia, aide, braveheart, dov,
Nearest to construction: projects, expressway, skyscraper, elimination, bunch, frobisher, building, canal,
Nearest to engine: petrol, combustion, engines, bcs, cordobas, chassis, search, steering,
Nearest to consists: consisting, consist, composed, extensive, includes, score, serres, lying,
Nearest to existence: personages, provability, ontological, ant, tilapia, shimura, tagus, disks,
Nearest to troops: sanatorium, clad, freed, rebuff, feeder, frenchmen, glanville, needles,
Nearest to egypt: egyptian, ancient, pyramids, promised, height, postscript, assistance, bloodthirsty,
Epoch 7/10 Iteration: 77100 Avg. Training loss: 4.0402 0.1610 sec/batch
Epoch 7/10 Iteration: 77200 Avg. Training loss: 3.9734 0.1604 sec/batch
Epoch 7/10 Iteration: 77300 Avg. Training loss: 3.9194 0.1632 sec/batch
Epoch 7/10 Iteration: 77400 Avg. Training loss: 4.0050 0.1600 sec/batch
Epoch 7/10 Iteration: 77500 Avg. Training loss: 3.9764 0.1602 sec/batch
Epoch 7/10 Iteration: 77600 Avg. Training loss: 3.9744 0.1644 sec/batch
Epoch 7/10 Iteration: 77700 Avg. Training loss: 3.9741 0.1664 sec/batch
Epoch 7/10 Iteration: 77800 Avg. Training loss: 4.0091 0.1632 sec/batch
Epoch 7/10 Iteration: 77900 Avg. Training loss: 3.9824 0.1611 sec/batch
Epoch 7/10 Iteration: 78000 Avg. Training loss: 3.9713 0.1732 sec/batch
Nearest to however: taiko, sacco, hellbender, participates, overdue, tuyere, scholars, goiter,
Nearest to no: longer, there, whatsoever, import, exists, yes, consensus, duplicate,
Nearest to were: they, these, bodies, originally, bantu, nuit, stolen, tailless,
Nearest to two: nd, nov, equ, sheng, folic, lactantius, guarantees, jafar,
Nearest to called: so, sometimes, investigates, often, spinach, also, kirkcaldy, japh,
Nearest to new: york, zealand, papua, caledonia, orleans, testament, jersey, radically,
Nearest to as: well, such, referred, known, regarded, result, taft, described,
Nearest to would: otherwise, announced, be, later, hoped, become, seem, moranis,
Nearest to frac: right, cdot, zam, left, dt, schwarzschild, bodhisattvas, sin,
Nearest to defense: vultures, defence, healing, employed, aide, pannonia, dov, braveheart,
Nearest to construction: projects, skyscraper, expressway, bunch, orgy, frobisher, intruder, elimination,
Nearest to engine: petrol, engines, combustion, bcs, chassis, cordobas, automobile, cooled,
Nearest to consists: consist, consisting, composed, extensive, includes, magistrate, serres, lying,
Nearest to existence: personages, provability, ontological, tilapia, ant, tagus, brummell, reborn,
Nearest to troops: sanatorium, clad, freed, rebuff, frenchmen, army, feeder, glanville,
Nearest to egypt: egyptian, ancient, pyramids, height, promised, postscript, sars, pharaoh,
Epoch 7/10 Iteration: 78100 Avg. Training loss: 3.9080 0.1850 sec/batch
Epoch 7/10 Iteration: 78200 Avg. Training loss: 3.9416 0.1887 sec/batch
Epoch 7/10 Iteration: 78300 Avg. Training loss: 3.9310 0.1872 sec/batch
Epoch 7/10 Iteration: 78400 Avg. Training loss: 3.9481 0.1805 sec/batch
Epoch 7/10 Iteration: 78500 Avg. Training loss: 3.9537 0.1625 sec/batch
Epoch 7/10 Iteration: 78600 Avg. Training loss: 3.9326 0.1659 sec/batch
Epoch 7/10 Iteration: 78700 Avg. Training loss: 4.0067 0.1796 sec/batch
Epoch 7/10 Iteration: 78800 Avg. Training loss: 3.7041 0.1703 sec/batch
Epoch 7/10 Iteration: 78900 Avg. Training loss: 3.8763 0.1670 sec/batch
Epoch 7/10 Iteration: 79000 Avg. Training loss: 3.8544 0.1640 sec/batch
Nearest to however: sacco, taiko, overdue, hellbender, participates, pdpa, noted, scholars,
Nearest to no: longer, there, consensus, whatsoever, import, exists, symphony, yes,
Nearest to were: they, these, nuit, bodies, originally, stolen, tailless, benchers,
Nearest to two: equ, nd, nov, zero, lazzeri, jafar, spender, lactantius,
Nearest to called: so, sometimes, investigates, often, also, kirkcaldy, spinach, oriente,
Nearest to new: york, zealand, papua, orleans, caledonia, testament, jersey, knopf,
Nearest to as: well, such, known, referred, regarded, result, taft, described,
Nearest to would: otherwise, announced, later, be, hoped, become, moranis, seem,
Nearest to frac: right, cdot, zam, dt, schwarzschild, bodhisattvas, left, sin,
Nearest to defense: vultures, healing, defence, aide, employed, pannonia, department, dov,
Nearest to construction: projects, expressway, skyscraper, elimination, orgy, under, intruder, bunch,
Nearest to engine: combustion, engines, petrol, bcs, chassis, steering, cooled, cordobas,
Nearest to consists: consisting, consist, composed, extensive, includes, magistrate, serres, lying,
Nearest to existence: personages, provability, tilapia, ontological, lorry, ant, qualitatively, tagus,
Nearest to troops: sanatorium, clad, freed, frenchmen, rebuff, army, soldiers, sieur,
Nearest to egypt: egyptian, ancient, pyramids, height, promised, pharaoh, bloodthirsty, sars,
Epoch 7/10 Iteration: 79100 Avg. Training loss: 3.9716 0.1753 sec/batch
Epoch 7/10 Iteration: 79200 Avg. Training loss: 4.0049 0.1629 sec/batch
Epoch 7/10 Iteration: 79300 Avg. Training loss: 3.9937 0.1843 sec/batch
Epoch 7/10 Iteration: 79400 Avg. Training loss: 4.0177 0.1833 sec/batch
Epoch 7/10 Iteration: 79500 Avg. Training loss: 4.0466 0.1976 sec/batch
Epoch 7/10 Iteration: 79600 Avg. Training loss: 4.0368 0.1842 sec/batch
Epoch 7/10 Iteration: 79700 Avg. Training loss: 4.0035 0.1638 sec/batch
Epoch 7/10 Iteration: 79800 Avg. Training loss: 3.9187 0.1684 sec/batch
Epoch 7/10 Iteration: 79900 Avg. Training loss: 3.9197 0.1670 sec/batch
Epoch 7/10 Iteration: 80000 Avg. Training loss: 3.9605 0.1663 sec/batch
Nearest to however: taiko, sacco, hellbender, overdue, participates, pdpa, moves, noted,
Nearest to no: longer, there, whatsoever, consensus, exists, symphony, yes, duplicate,
Nearest to were: they, benchers, these, bodies, stolen, bantu, outmaneuvered, taa,
Nearest to two: zero, nov, nd, equ, jafar, folic, bids, lactantius,
Nearest to called: so, sometimes, investigates, often, also, japh, spinach, oriente,
Nearest to new: york, zealand, papua, orleans, caledonia, jersey, testament, radically,
Nearest to as: well, such, known, referred, regarded, result, described, taft,
Nearest to would: otherwise, be, announced, later, hoped, become, seem, moranis,
Nearest to frac: right, cdot, zam, dt, left, schwarzschild, bodhisattvas, sin,
Nearest to defense: vultures, healing, defence, aide, pannonia, employed, dov, department,
Nearest to construction: projects, expressway, skyscraper, elimination, bunch, building, under, intruder,
Nearest to engine: combustion, engines, petrol, bcs, chassis, cordobas, steering, automobile,
Nearest to consists: consist, consisting, composed, of, includes, extensive, magistrate, serres,
Nearest to existence: personages, provability, tilapia, ontological, ant, lorry, reborn, tagus,
Nearest to troops: sanatorium, clad, freed, withdrawal, rebuff, frenchmen, soldiers, maghreb,
Nearest to egypt: egyptian, ancient, height, pyramids, promised, metallurgy, bloodthirsty, postscript,
Epoch 7/10 Iteration: 80100 Avg. Training loss: 3.9001 0.1940 sec/batch
Epoch 7/10 Iteration: 80200 Avg. Training loss: 3.9666 0.1994 sec/batch
Epoch 7/10 Iteration: 80300 Avg. Training loss: 3.9881 0.1946 sec/batch
Epoch 7/10 Iteration: 80400 Avg. Training loss: 3.9675 0.1653 sec/batch
Epoch 7/10 Iteration: 80500 Avg. Training loss: 3.9576 0.1637 sec/batch
Epoch 7/10 Iteration: 80600 Avg. Training loss: 3.7990 0.1641 sec/batch
Epoch 7/10 Iteration: 80700 Avg. Training loss: 3.8493 0.1878 sec/batch
Epoch 7/10 Iteration: 80800 Avg. Training loss: 3.9870 0.1949 sec/batch
Epoch 7/10 Iteration: 80900 Avg. Training loss: 3.8679 0.1966 sec/batch
Epoch 7/10 Iteration: 81000 Avg. Training loss: 3.9693 0.1987 sec/batch
Nearest to however: sacco, taiko, hellbender, overdue, participates, noted, pdpa, belied,
Nearest to no: longer, there, whatsoever, consensus, exists, yes, symphony, evidence,
Nearest to were: they, these, symphonic, stolen, bantu, benchers, originally, bodies,
Nearest to two: nd, equ, zero, nov, lazzeri, folic, bids, sheng,
Nearest to called: so, sometimes, investigates, also, often, oriente, japh, spinach,
Nearest to new: york, zealand, papua, orleans, caledonia, jersey, testament, radically,
Nearest to as: well, such, known, referred, regarded, result, described, taft,
Nearest to would: otherwise, announced, be, later, hoped, become, moranis, seem,
Nearest to frac: right, cdot, zam, dt, schwarzschild, bodhisattvas, sin, left,
Nearest to defense: vultures, pannonia, healing, defence, aide, employed, department, dov,
Nearest to construction: projects, expressway, skyscraper, bunch, elimination, building, materials, frobisher,
Nearest to engine: engines, petrol, combustion, bcs, chassis, cordobas, automobile, search,
Nearest to consists: consisting, consist, composed, of, extensive, magistrate, includes, serres,
Nearest to existence: personages, provability, tilapia, ontological, ant, lorry, pervading, brummell,
Nearest to troops: sanatorium, clad, freed, withdrawal, frenchmen, rebuff, army, glanville,
Nearest to egypt: egyptian, ancient, height, promised, pyramids, pharaoh, nubia, bloodthirsty,
Epoch 7/10 Iteration: 81100 Avg. Training loss: 3.9707 0.2028 sec/batch
Epoch 7/10 Iteration: 81200 Avg. Training loss: 3.9761 0.1926 sec/batch






    



---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-150-6abe39173d5e> in <module>()
     19             feed = {inputs: x,
     20                     labels: np.array(y)[:, None]}
---> 21             train_loss, _ = sess.run([cost, optimizer], feed_dict=feed)
     22 
     23             loss += train_loss

/home/tr/anaconda3/envs/tensorflow35/lib/python3.5/site-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
    765     try:
    766       result = self._run(None, fetches, feed_dict, options_ptr,
--> 767                          run_metadata_ptr)
    768       if run_metadata:
    769         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

/home/tr/anaconda3/envs/tensorflow35/lib/python3.5/site-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
    963     if final_fetches or final_targets:
    964       results = self._do_run(handle, final_targets, final_fetches,
--> 965                              feed_dict_string, options, run_metadata)
    966     else:
    967       results = []

/home/tr/anaconda3/envs/tensorflow35/lib/python3.5/site-packages/tensorflow/python/client/session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
   1013     if handle is None:
   1014       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,
-> 1015                            target_list, options, run_metadata)
   1016     else:
   1017       return self._do_call(_prun_fn, self._session, handle, feed_dict,

/home/tr/anaconda3/envs/tensorflow35/lib/python3.5/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
   1020   def _do_call(self, fn, *args):
   1021     try:
-> 1022       return fn(*args)
   1023     except errors.OpError as e:
   1024       message = compat.as_text(e.message)

/home/tr/anaconda3/envs/tensorflow35/lib/python3.5/site-packages/tensorflow/python/client/session.py in _run_fn(session, feed_dict, fetch_list, target_list, options, run_metadata)
   1002         return tf_session.TF_Run(session, options,
   1003                                  feed_dict, fetch_list, target_list,
-> 1004                                  status, run_metadata)
   1005 
   1006     def _prun_fn(session, handle, feed_dict, fetch_list):

KeyboardInterrupt:

Restore the trained network if you need to:



In [151]:

    
with train_graph.as_default():
    saver = tf.train.Saver()

with tf.Session(graph=train_graph) as sess:
    saver.restore(sess, tf.train.latest_checkpoint('checkpoints'))
    embed_mat = sess.run(embedding)









    



---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
TypeError: expected bytes, NoneType found

During handling of the above exception, another exception occurred:

SystemError                               Traceback (most recent call last)
<ipython-input-151-4eeca6a379f0> in <module>()
      3 
      4 with tf.Session(graph=train_graph) as sess:
----> 5     saver.restore(sess, tf.train.latest_checkpoint('checkpoints'))
      6     embed_mat = sess.run(embedding)

/home/tr/anaconda3/envs/tensorflow35/lib/python3.5/site-packages/tensorflow/python/training/saver.py in restore(self, sess, save_path)
   1426       return
   1427     sess.run(self.saver_def.restore_op_name,
-> 1428              {self.saver_def.filename_tensor_name: save_path})
   1429 
   1430   @staticmethod

/home/tr/anaconda3/envs/tensorflow35/lib/python3.5/site-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
    765     try:
    766       result = self._run(None, fetches, feed_dict, options_ptr,
--> 767                          run_metadata_ptr)
    768       if run_metadata:
    769         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

/home/tr/anaconda3/envs/tensorflow35/lib/python3.5/site-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
    963     if final_fetches or final_targets:
    964       results = self._do_run(handle, final_targets, final_fetches,
--> 965                              feed_dict_string, options, run_metadata)
    966     else:
    967       results = []

/home/tr/anaconda3/envs/tensorflow35/lib/python3.5/site-packages/tensorflow/python/client/session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
   1013     if handle is None:
   1014       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,
-> 1015                            target_list, options, run_metadata)
   1016     else:
   1017       return self._do_call(_prun_fn, self._session, handle, feed_dict,

/home/tr/anaconda3/envs/tensorflow35/lib/python3.5/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
   1020   def _do_call(self, fn, *args):
   1021     try:
-> 1022       return fn(*args)
   1023     except errors.OpError as e:
   1024       message = compat.as_text(e.message)

/home/tr/anaconda3/envs/tensorflow35/lib/python3.5/site-packages/tensorflow/python/client/session.py in _run_fn(session, feed_dict, fetch_list, target_list, options, run_metadata)
   1002         return tf_session.TF_Run(session, options,
   1003                                  feed_dict, fetch_list, target_list,
-> 1004                                  status, run_metadata)
   1005 
   1006     def _prun_fn(session, handle, feed_dict, fetch_list):

SystemError: <built-in function TF_Run> returned a result with an error set

Visualizing the word vectors

Below we'll use T-SNE to visualize how our high-dimensional word vectors cluster together. T-SNE is used to project these vectors into two dimensions while preserving local stucture. Check out this post from Christopher Olah to learn more about T-SNE and other ways to visualize high-dimensional data.



In [ ]:

    
%matplotlib inline
%config InlineBackend.figure_format = 'retina'

import matplotlib.pyplot as plt
from sklearn.manifold import TSNE



In [155]:

    
viz_words = 500
tsne = TSNE()
embed_tsne = tsne.fit_transform(embed_mat[:viz_words, :])









    



---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-155-a4718e25bc97> in <module>()
      1 viz_words = 500
      2 tsne = TSNE()
----> 3 embed_tsne = tsne.fit_transform(embed_mat[:viz_words, :])

NameError: name 'embed_mat' is not defined



In [ ]:

    
fig, ax = plt.subplots(figsize=(14, 14))
for idx in range(viz_words):
    plt.scatter(*embed_tsne[idx, :], color='steelblue')
    plt.annotate(int_to_vocab[idx], (embed_tsne[idx, 0], embed_tsne[idx, 1]), alpha=0.7)



In [ ]: