End-to-end Masked Language Modeling with BERT
Author: Ankur Singh
Date created: 2020/09/18
Last modified: 2024/03/15
Description: Implement a Masked Language Model (MLM) with BERT and fine-tune it on the IMDB Reviews dataset.
View in Colab •
GitHub source
Introduction
Masked Language Modeling is a fill-in-the-blank task, where a model uses the context words surrounding a mask token to try to predict what the masked word should be.
For an input that contains one or more mask tokens, the model will generate the most likely substitution for each.
Example:
Masked language modeling is a great way to train a language model in a self-supervised setting (without human-annotated labels). Such a model can then be fine-tuned to accomplish various supervised NLP tasks.
This example teaches you how to build a BERT model from scratch, train it with the masked language modeling task, and then fine-tune this model on a sentiment classification task.
We will use the Keras TextVectorization and MultiHeadAttention layers to create a BERT Transformer-Encoder network architecture.
Note: This example should be run with tf-nightly.
Setup
Install tf-nightly via pip install tf-nightly.
import os
os.environ["KERAS_BACKEND"] = "torch"
import keras_hub
import keras
from keras import layers
from keras.layers import TextVectorization
from dataclasses import dataclass
import pandas as pd
import numpy as np
import glob
import re
from pprint import pprint
Set-up Configuration
@dataclass
class Config:
MAX_LEN = 256
BATCH_SIZE = 32
LR = 0.001
VOCAB_SIZE = 30000
EMBED_DIM = 128
NUM_HEAD = 8
FF_DIM = 128
NUM_LAYERS = 1
config = Config()
Load the data
We will first download the IMDB data and load into a Pandas dataframe.
!curl -O https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
!tar -xf aclImdb_v1.tar.gz
```
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
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0 0 0 0 0 0 0 0 --:--:-- 0:00:01 --:--:-- 0
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```
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```python
def get_text_list_from_files(files):
text_list = []
for name in files:
with open(name) as f:
for line in f:
text_list.append(line)
return text_list
def get_data_from_text_files(folder_name):
pos_files = glob.glob("aclImdb/" + folder_name + "/pos/*.txt")
pos_texts = get_text_list_from_files(pos_files)
neg_files = glob.glob("aclImdb/" + folder_name + "/neg/*.txt")
neg_texts = get_text_list_from_files(neg_files)
df = pd.DataFrame(
{
"review": pos_texts + neg_texts,
"sentiment": [0] * len(pos_texts) + [1] * len(neg_texts),
}
)
df = df.sample(len(df)).reset_index(drop=True)
return df
train_df = get_data_from_text_files("train")
test_df = get_data_from_text_files("test")
all_data = pd.concat([train_df, test_df], ignore_index=True)
Dataset preparation
We will use the TextVectorization layer to vectorize the text into integer token ids. It transforms a batch of strings into either a sequence of token indices (one sample = 1D array of integer token indices, in order) or a dense representation (one sample = 1D array of float values encoding an unordered set of tokens).
Below, we define 3 preprocessing functions.
The get_vectorize_layer function builds the TextVectorization layer.
The encode function encodes raw text into integer token ids.
The get_masked_input_and_labels function will mask input token ids. It masks 15% of all input tokens in each sequence at random.
import tensorflow as tf
def custom_standardization(input_data):
lowercase = tf.strings.lower(input_data)
stripped_html = tf.strings.regex_replace(lowercase, "<br />", " ")
return tf.strings.regex_replace(
stripped_html, "[%s]" % re.escape("!#$%&'()*+,-./:;<=>?@\^_`{|}~"), ""
)
def get_vectorize_layer(texts, vocab_size, max_seq, special_tokens=["[MASK]"]):
"""Build Text vectorization layer
Args:
texts (list): List of string i.e input texts
vocab_size (int): vocab size
max_seq (int): Maximum sequence length.
special_tokens (list, optional): List of special tokens. Defaults to ['[MASK]'].
Returns:
layers.Layer: Return TextVectorization Keras Layer
"""
vectorize_layer = TextVectorization(
max_tokens=vocab_size,
output_mode="int",
standardize=custom_standardization,
output_sequence_length=max_seq,
)
vectorize_layer.adapt(texts)
vocab = vectorize_layer.get_vocabulary()
vocab = vocab[2 : vocab_size - len(special_tokens)] + ["[mask]"]
vectorize_layer.set_vocabulary(vocab)
return vectorize_layer
vectorize_layer = get_vectorize_layer(
all_data.review.values.tolist(),
config.VOCAB_SIZE,
config.MAX_LEN,
special_tokens=["[mask]"],
)
mask_token_id = vectorize_layer(["[mask]"]).numpy()[0][0]
def encode(texts):
encoded_texts = vectorize_layer(texts)
return encoded_texts.numpy()
def get_masked_input_and_labels(encoded_texts):
inp_mask = np.random.rand(*encoded_texts.shape) < 0.15
inp_mask[encoded_texts <= 2] = False
labels = -1 * np.ones(encoded_texts.shape, dtype=int)
labels[inp_mask] = encoded_texts[inp_mask]
encoded_texts_masked = np.copy(encoded_texts)
inp_mask_2mask = inp_mask & (np.random.rand(*encoded_texts.shape) < 0.90)
encoded_texts_masked[inp_mask_2mask] = (
mask_token_id
)
inp_mask_2random = inp_mask_2mask & (np.random.rand(*encoded_texts.shape) < 1 / 9)
encoded_texts_masked[inp_mask_2random] = np.random.randint(
3, mask_token_id, inp_mask_2random.sum()
)
sample_weights = np.ones(labels.shape)
sample_weights[labels == -1] = 0
y_labels = np.copy(encoded_texts)
return encoded_texts_masked, y_labels, sample_weights
x_train = encode(train_df.review.values)
y_train = train_df.sentiment.values
train_classifier_ds = (
tf.data.Dataset.from_tensor_slices((x_train, y_train))
.shuffle(1000)
.batch(config.BATCH_SIZE)
)
x_test = encode(test_df.review.values)
y_test = test_df.sentiment.values
test_classifier_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(
config.BATCH_SIZE
)
test_raw_classifier_ds = test_df
x_all_review = encode(all_data.review.values)
x_masked_train, y_masked_labels, sample_weights = get_masked_input_and_labels(
x_all_review
)
mlm_ds = tf.data.Dataset.from_tensor_slices(
(x_masked_train, y_masked_labels, sample_weights)
)
mlm_ds = mlm_ds.shuffle(1000).batch(config.BATCH_SIZE)
Create BERT model (Pretraining Model) for masked language modeling
We will create a BERT-like pretraining model architecture using the MultiHeadAttention layer. It will take token ids as inputs (including masked tokens) and it will predict the correct ids for the masked input tokens.
def bert_module(query, key, value, i):
attention_output = layers.MultiHeadAttention(
num_heads=config.NUM_HEAD,
key_dim=config.EMBED_DIM // config.NUM_HEAD,
name="encoder_{}_multiheadattention".format(i),
)(query, key, value)
attention_output = layers.Dropout(0.1, name="encoder_{}_att_dropout".format(i))(
attention_output
)
attention_output = layers.LayerNormalization(
epsilon=1e-6, name="encoder_{}_att_layernormalization".format(i)
)(query + attention_output)
ffn = keras.Sequential(
[
layers.Dense(config.FF_DIM, activation="relu"),
layers.Dense(config.EMBED_DIM),
],
name="encoder_{}_ffn".format(i),
)
ffn_output = ffn(attention_output)
ffn_output = layers.Dropout(0.1, name="encoder_{}_ffn_dropout".format(i))(
ffn_output
)
sequence_output = layers.LayerNormalization(
epsilon=1e-6, name="encoder_{}_ffn_layernormalization".format(i)
)(attention_output + ffn_output)
return sequence_output
loss_fn = keras.losses.SparseCategoricalCrossentropy(reduction=None)
loss_tracker = keras.metrics.Mean(name="loss")
class MaskedLanguageModel(keras.Model):
def compute_loss(self, x=None, y=None, y_pred=None, sample_weight=None):
loss = loss_fn(y, y_pred, sample_weight)
loss_tracker.update_state(loss, sample_weight=sample_weight)
return keras.ops.sum(loss)
def compute_metrics(self, x, y, y_pred, sample_weight):
return {"loss": loss_tracker.result()}
@property
def metrics(self):
return [loss_tracker]
def create_masked_language_bert_model():
inputs = layers.Input((config.MAX_LEN,), dtype="int64")
word_embeddings = layers.Embedding(
config.VOCAB_SIZE, config.EMBED_DIM, name="word_embedding"
)(inputs)
position_embeddings = keras_hub.layers.PositionEmbedding(
sequence_length=config.MAX_LEN
)(word_embeddings)
embeddings = word_embeddings + position_embeddings
encoder_output = embeddings
for i in range(config.NUM_LAYERS):
encoder_output = bert_module(encoder_output, encoder_output, encoder_output, i)
mlm_output = layers.Dense(config.VOCAB_SIZE, name="mlm_cls", activation="softmax")(
encoder_output
)
mlm_model = MaskedLanguageModel(inputs, mlm_output, name="masked_bert_model")
optimizer = keras.optimizers.Adam(learning_rate=config.LR)
mlm_model.compile(optimizer=optimizer)
return mlm_model
id2token = dict(enumerate(vectorize_layer.get_vocabulary()))
token2id = {y: x for x, y in id2token.items()}
class MaskedTextGenerator(keras.callbacks.Callback):
def __init__(self, sample_tokens, top_k=5):
self.sample_tokens = sample_tokens
self.k = top_k
def decode(self, tokens):
return " ".join([id2token[t] for t in tokens if t != 0])
def convert_ids_to_tokens(self, id):
return id2token[id]
def on_epoch_end(self, epoch, logs=None):
prediction = self.model.predict(self.sample_tokens)
masked_index = np.where(self.sample_tokens == mask_token_id)
masked_index = masked_index[1]
mask_prediction = prediction[0][masked_index]
top_indices = mask_prediction[0].argsort()[-self.k :][::-1]
values = mask_prediction[0][top_indices]
for i in range(len(top_indices)):
p = top_indices[i]
v = values[i]
tokens = np.copy(sample_tokens[0])
tokens[masked_index[0]] = p
result = {
"input_text": self.decode(sample_tokens[0].numpy()),
"prediction": self.decode(tokens),
"probability": v,
"predicted mask token": self.convert_ids_to_tokens(p),
}
pprint(result)
sample_tokens = vectorize_layer(["I have watched this [mask] and it was awesome"])
generator_callback = MaskedTextGenerator(sample_tokens.numpy())
bert_masked_model = create_masked_language_bert_model()
bert_masked_model.summary()
Model: "masked_bert_model"
┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓
┃ Layer (type) ┃ Output Shape ┃ Param # ┃ Connected to ┃
┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩
│ input_layer │ (None, 256) │ 0 │ - │
│ (InputLayer) │ │ │ │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ word_embedding │ (None, 256, 128) │ 3,840,000 │ input_layer[0][0] │
│ (Embedding) │ │ │ │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ position_embedding │ (None, 256, 128) │ 32,768 │ word_embedding[0… │
│ (PositionEmbedding) │ │ │ │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ add (Add) │ (None, 256, 128) │ 0 │ word_embedding[0… │
│ │ │ │ position_embeddi… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ encoder_0_multihea… │ (None, 256, 128) │ 66,048 │ add[0][0], │
│ (MultiHeadAttentio… │ │ │ add[0][0], │
│ │ │ │ add[0][0] │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ encoder_0_att_drop… │ (None, 256, 128) │ 0 │ encoder_0_multih… │
│ (Dropout) │ │ │ │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ add_1 (Add) │ (None, 256, 128) │ 0 │ add[0][0], │
│ │ │ │ encoder_0_att_dr… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ encoder_0_att_laye… │ (None, 256, 128) │ 256 │ add_1[0][0] │
│ (LayerNormalizatio… │ │ │ │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ encoder_0_ffn │ (None, 256, 128) │ 33,024 │ encoder_0_att_la… │
│ (Sequential) │ │ │ │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ encoder_0_ffn_drop… │ (None, 256, 128) │ 0 │ encoder_0_ffn[0]… │
│ (Dropout) │ │ │ │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ add_2 (Add) │ (None, 256, 128) │ 0 │ encoder_0_att_la… │
│ │ │ │ encoder_0_ffn_dr… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ encoder_0_ffn_laye… │ (None, 256, 128) │ 256 │ add_2[0][0] │
│ (LayerNormalizatio… │ │ │ │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ mlm_cls (Dense) │ (None, 256, │ 3,870,000 │ encoder_0_ffn_la… │
│ │ 30000) │ │ │
└─────────────────────┴───────────────────┴────────────┴───────────────────┘
Total params: 7,842,352 (29.92 MB)
Trainable params: 7,842,352 (29.92 MB)
Non-trainable params: 0 (0.00 B)
Train and Save
bert_masked_model.fit(mlm_ds, epochs=5, callbacks=[generator_callback])
bert_masked_model.save("bert_mlm_imdb.keras")
1/16 ━[37m━━━━━━━━━━━━━━━━━━━ 3:02 12s/step - loss: 10.3103
```
```
2/16 ━━[37m━━━━━━━━━━━━━━━━━━ 3:31 15s/step - loss: 10.2979
```
```
3/16 ━━━[37m━━━━━━━━━━━━━━━━━ 3:25 16s/step - loss: 10.2859
```
```
4/16 ━━━━━[37m━━━━━━━━━━━━━━━ 3:14 16s/step - loss: 10.2727
```
```
5/16 ━━━━━━[37m━━━━━━━━━━━━━━ 2:57 16s/step - loss: 10.2564
```
```
6/16 ━━━━━━━[37m━━━━━━━━━━━━━ 2:42 16s/step - loss: 10.2378
```
```
7/16 ━━━━━━━━[37m━━━━━━━━━━━━ 2:26 16s/step - loss: 10.2182
```
```
8/16 ━━━━━━━━━━[37m━━━━━━━━━━ 2:10 16s/step - loss: 10.1975
```
```
9/16 ━━━━━━━━━━━[37m━━━━━━━━━ 1:55 16s/step - loss: 10.1745
```
```
10/16 ━━━━━━━━━━━━[37m━━━━━━━━ 1:39 17s/step - loss: 10.1503
```
```
11/16 ━━━━━━━━━━━━━[37m━━━━━━━ 1:23 17s/step - loss: 10.1254
```
```
12/16 ━━━━━━━━━━━━━━━[37m━━━━━ 1:07 17s/step - loss: 10.0993
```
```
13/16 ━━━━━━━━━━━━━━━━[37m━━━━ 50s 17s/step - loss: 10.0726
```
```
14/16 ━━━━━━━━━━━━━━━━━[37m━━━ 33s 17s/step - loss: 10.0452
```
```
15/16 ━━━━━━━━━━━━━━━━━━[37m━━ 16s 17s/step - loss: 10.0174
```
```
16/16 ━━━━━━━━━━━━━━━━━━━━ 0s 17s/step - loss: 9.9899
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 81ms/step
```
```
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 82ms/step
```
{'input_text': 'i have watched this [mask] and it was awesome',
'predicted mask token': 'a',
'prediction': 'i have watched this a and it was awesome',
'probability': 0.0013674975}
{'input_text': 'i have watched this [mask] and it was awesome',
'predicted mask token': 'i',
'prediction': 'i have watched this i and it was awesome',
'probability': 0.0012694978}
{'input_text': 'i have watched this [mask] and it was awesome',
'predicted mask token': 'is',
'prediction': 'i have watched this is and it was awesome',
'probability': 0.0012668626}
{'input_text': 'i have watched this [mask] and it was awesome',
'predicted mask token': 'to',
'prediction': 'i have watched this to and it was awesome',
'probability': 0.0012651902}
{'input_text': 'i have watched this [mask] and it was awesome',
'predicted mask token': 'of',
'prediction': 'i have watched this of and it was awesome',
'probability': 0.0011966776}
```
16/16 ━━━━━━━━━━━━━━━━━━━━ 261s 17s/step - loss: 9.9656
Fine-tune a sentiment classification model
We will fine-tune our self-supervised model on a downstream task of sentiment classification. To do this, let's create a classifier by adding a pooling layer and a Dense layer on top of the pretrained BERT features.
mlm_model = keras.models.load_model(
"bert_mlm_imdb.keras", custom_objects={"MaskedLanguageModel": MaskedLanguageModel}
)
pretrained_bert_model = keras.Model(
mlm_model.input, mlm_model.get_layer("encoder_0_ffn_layernormalization").output
)
pretrained_bert_model.trainable = False
def create_classifier_bert_model():
inputs = layers.Input((config.MAX_LEN,), dtype="int64")
sequence_output = pretrained_bert_model(inputs)
pooled_output = layers.GlobalMaxPooling1D()(sequence_output)
hidden_layer = layers.Dense(64, activation="relu")(pooled_output)
outputs = layers.Dense(1, activation="sigmoid")(hidden_layer)
classifer_model = keras.Model(inputs, outputs, name="classification")
optimizer = keras.optimizers.Adam()
classifer_model.compile(
optimizer=optimizer, loss="binary_crossentropy", metrics=["accuracy"]
)
return classifer_model
classifer_model = create_classifier_bert_model()
classifer_model.summary()
classifer_model.fit(
train_classifier_ds,
epochs=5,
validation_data=test_classifier_ds,
)
pretrained_bert_model.trainable = True
optimizer = keras.optimizers.Adam()
classifer_model.compile(
optimizer=optimizer, loss="binary_crossentropy", metrics=["accuracy"]
)
classifer_model.fit(
train_classifier_ds,
epochs=5,
validation_data=test_classifier_ds,
)
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type) ┃ Output Shape ┃ Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ input_layer_2 (InputLayer) │ (None, 256) │ 0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ functional_3 (Functional) │ (None, 256, 128) │ 3,972,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_max_pooling1d │ (None, 128) │ 0 │
│ (GlobalMaxPooling1D) │ │ │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_2 (Dense) │ (None, 64) │ 8,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_3 (Dense) │ (None, 1) │ 65 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
Total params: 3,980,673 (15.19 MB)
Trainable params: 8,321 (32.50 KB)
Non-trainable params: 3,972,352 (15.15 MB)
1/8 ━━[37m━━━━━━━━━━━━━━━━━━ 0s 140ms/step - accuracy: 0.5312 - loss: 0.7599
```
```
2/8 ━━━━━[37m━━━━━━━━━━━━━━━ 1s 184ms/step - accuracy: 0.5703 - loss: 0.7296
```
```
3/8 ━━━━━━━[37m━━━━━━━━━━━━━ 0s 164ms/step - accuracy: 0.5851 - loss: 0.7164
```
```
4/8 ━━━━━━━━━━[37m━━━━━━━━━━ 0s 161ms/step - accuracy: 0.5794 - loss: 0.7125
```
```
5/8 ━━━━━━━━━━━━[37m━━━━━━━━ 0s 158ms/step - accuracy: 0.5685 - loss: 0.7105
```
```
6/8 ━━━━━━━━━━━━━━━[37m━━━━━ 0s 158ms/step - accuracy: 0.5589 - loss: 0.7090
```
```
7/8 ━━━━━━━━━━━━━━━━━[37m━━━ 0s 156ms/step - accuracy: 0.5504 - loss: 0.7080
```
```
8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 151ms/step - accuracy: 0.5426 - loss: 0.7076
```
```
8/8 ━━━━━━━━━━━━━━━━━━━━ 2s 288ms/step - accuracy: 0.5366 - loss: 0.7073 - val_accuracy: 0.4920 - val_loss: 0.6975
1/8 ━━[37m━━━━━━━━━━━━━━━━━━ 3s 436ms/step - accuracy: 0.5000 - loss: 0.7119
```
```
2/8 ━━━━━[37m━━━━━━━━━━━━━━━ 3s 534ms/step - accuracy: 0.5469 - loss: 0.6903
```
```
3/8 ━━━━━━━[37m━━━━━━━━━━━━━ 2s 472ms/step - accuracy: 0.5660 - loss: 0.6913
```
```
4/8 ━━━━━━━━━━[37m━━━━━━━━━━ 1s 461ms/step - accuracy: 0.5671 - loss: 0.7032
```
```
5/8 ━━━━━━━━━━━━[37m━━━━━━━━ 1s 459ms/step - accuracy: 0.5636 - loss: 0.7116
```
```
6/8 ━━━━━━━━━━━━━━━[37m━━━━━ 0s 468ms/step - accuracy: 0.5626 - loss: 0.7156
```
```
7/8 ━━━━━━━━━━━━━━━━━[37m━━━ 0s 476ms/step - accuracy: 0.5600 - loss: 0.7183
```
```
8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 476ms/step - accuracy: 0.5580 - loss: 0.7198
```
```
8/8 ━━━━━━━━━━━━━━━━━━━━ 5s 650ms/step - accuracy: 0.5565 - loss: 0.7210 - val_accuracy: 0.5160 - val_loss: 0.6895
</div>
---
## Create an end-to-end model and evaluate it
When you want to deploy a model, it's best if it already includes its preprocessing
pipeline, so that you don't have to reimplement the preprocessing logic in your
production environment. Let's create an end-to-end model that incorporates
the `TextVectorization` layer inside evalaute method, and let's evaluate. We will pass raw strings as input.
```python
# We create a custom Model to override the evaluate method so
# that it first pre-process text data
class ModelEndtoEnd(keras.Model):
def evaluate(self, inputs):
features = encode(inputs.review.values)
labels = inputs.sentiment.values
test_classifier_ds = (
tf.data.Dataset.from_tensor_slices((features, labels))
.shuffle(1000)
.batch(config.BATCH_SIZE)
)
return super().evaluate(test_classifier_ds)
# Build the model
def build(self, input_shape):
self.built = True
def get_end_to_end(model):
inputs = classifer_model.inputs[0]
outputs = classifer_model.outputs
end_to_end_model = ModelEndtoEnd(inputs, outputs, name="end_to_end_model")
optimizer = keras.optimizers.Adam(learning_rate=config.LR)
end_to_end_model.compile(
optimizer=optimizer, loss="binary_crossentropy", metrics=["accuracy"]
)
return end_to_end_model
end_to_end_classification_model = get_end_to_end(classifer_model)
# Pass raw text dataframe to the model
end_to_end_classification_model.evaluate(test_raw_classifier_ds)
1/8 ━━[37m━━━━━━━━━━━━━━━━━━ 0s 138ms/step - accuracy: 0.6875 - loss: 0.6684
```
```
2/8 ━━━━━[37m━━━━━━━━━━━━━━━ 1s 225ms/step - accuracy: 0.6250 - loss: 0.6761
```
```
3/8 ━━━━━━━[37m━━━━━━━━━━━━━ 0s 190ms/step - accuracy: 0.5833 - loss: 0.6820
```
```
4/8 ━━━━━━━━━━[37m━━━━━━━━━━ 0s 184ms/step - accuracy: 0.5605 - loss: 0.6848
```
```
5/8 ━━━━━━━━━━━━[37m━━━━━━━━ 0s 178ms/step - accuracy: 0.5422 - loss: 0.6871
```
```
6/8 ━━━━━━━━━━━━━━━[37m━━━━━ 0s 174ms/step - accuracy: 0.5352 - loss: 0.6880
```
```
7/8 ━━━━━━━━━━━━━━━━━[37m━━━ 0s 169ms/step - accuracy: 0.5320 - loss: 0.6883
```
```
8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 165ms/step - accuracy: 0.5300 - loss: 0.6885
```
```
8/8 ━━━━━━━━━━━━━━━━━━━━ 1s 166ms/step - accuracy: 0.5285 - loss: 0.6886
```
[0.6894814372062683, 0.515999972820282]
