使用 BERT 的端到端 Masked Language Modeling
作者： Ankur Singh
创建日期 2020/09/18
最后修改日期 2024/03/15
描述： 使用 BERT 实现 Masked Language Model (MLM)，并在 IMDB 评论数据集上对其进行微调。
简介
Masked Language Modeling 是一种填空任务，其中模型使用 mask 标记周围的上下文词语来尝试预测被 mask 的词语应该是什么。
对于包含一个或多个 mask 标记的输入，模型将为每个标记生成最可能的替换。
示例
- 输入："I have watched this [MASK] and it was awesome."
- 输出："I have watched this movie and it was awesome."
Masked language modeling 是在自监督设置（无需人工标注标签）中训练语言模型的好方法。然后，可以对这样的模型进行微调，以完成各种监督式 NLP 任务。
本示例教您如何从头开始构建 BERT 模型，使用 masked language modeling 任务对其进行训练，然后在情感分类任务上微调此模型。
我们将使用 Keras TextVectorization 和 MultiHeadAttention 层来创建 BERT Transformer-Encoder 网络架构。
注意：本示例应使用 tf-nightly 运行。
安装
通过 pip install tf-nightly 安装 tf-nightly。
import os
os.environ["KERAS_BACKEND"] = "torch" # or jax, or tensorflow
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
设置配置
@dataclass
class Config:
MAX_LEN = 256
BATCH_SIZE = 32
LR = 0.001
VOCAB_SIZE = 30000
EMBED_DIM = 128
NUM_HEAD = 8 # used in bert model
FF_DIM = 128 # used in bert model
NUM_LAYERS = 1
config = Config()
加载数据
我们将首先下载 IMDB 数据并加载到 Pandas 数据帧中。
!curl -O https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
!tar -xf aclImdb_v1.tar.gz
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100 80.2M 100 80.2M 0 0 378k 0 0:03:37 0:03:37 –:–:– 473k
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)
数据集准备
我们将使用 TextVectorization 层将文本向量化为整数 token id。它将一批字符串转换为 token 索引序列（一个样本 = 整数 token 索引的 1D 数组，按顺序排列）或密集表示（一个样本 = 浮点数值的 1D 数组，编码无序的 token 集）。
下面，我们定义了 3 个预处理函数。
get_vectorize_layer函数构建TextVectorization层。encode函数将原始文本编码为整数 token id。get_masked_input_and_labels函数将 mask 输入 token id。它随机 mask 每个序列中 15% 的输入 token。
# For data pre-processing and tf.data.Dataset
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)
# Insert mask token in vocabulary
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]"],
)
# Get mask token id for masked language model
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):
# 15% BERT masking
inp_mask = np.random.rand(*encoded_texts.shape) < 0.15
# Do not mask special tokens
inp_mask[encoded_texts <= 2] = False
# Set targets to -1 by default, it means ignore
labels = -1 * np.ones(encoded_texts.shape, dtype=int)
# Set labels for masked tokens
labels[inp_mask] = encoded_texts[inp_mask]
# Prepare input
encoded_texts_masked = np.copy(encoded_texts)
# Set input to [MASK] which is the last token for the 90% of tokens
# This means leaving 10% unchanged
inp_mask_2mask = inp_mask & (np.random.rand(*encoded_texts.shape) < 0.90)
encoded_texts_masked[inp_mask_2mask] = (
mask_token_id # mask token is the last in the dict
)
# Set 10% to a random token
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()
)
# Prepare sample_weights to pass to .fit() method
sample_weights = np.ones(labels.shape)
sample_weights[labels == -1] = 0
# y_labels would be same as encoded_texts i.e input tokens
y_labels = np.copy(encoded_texts)
return encoded_texts_masked, y_labels, sample_weights
# We have 25000 examples for training
x_train = encode(train_df.review.values) # encode reviews with vectorizer
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)
)
# We have 25000 examples for testing
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
)
# Dataset for end to end model input (will be used at the end)
test_raw_classifier_ds = test_df
# Prepare data for masked language model
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)
创建用于 masked language modeling 的 BERT 模型（预训练模型）
我们将使用 MultiHeadAttention 层创建一个类似 BERT 的预训练模型架构。它将 token id 作为输入（包括 masked token），并将预测 masked 输入 token 的正确 id。
def bert_module(query, key, value, i):
# Multi headed self-attention
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)
# Feed-forward layer
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 a dict mapping metric names to current value
return {"loss": loss_tracker.result()}
@property
def metrics(self):
# We list our `Metric` objects here so that `reset_states()` can be
# called automatically at the start of each epoch
# or at the start of `evaluate()`.
# If you don't implement this property, you have to call
# `reset_states()` yourself at the time of your choosing.
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)
训练和保存
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
微调情感分类模型
我们将对下游情感分类任务微调我们的自监督模型。为此，让我们通过在预训练的 BERT 特征之上添加池化层和 Dense 层来创建一个分类器。
# Load pretrained bert model
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
)
# Freeze it
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()
# Train the classifier with frozen BERT stage
classifer_model.fit(
train_classifier_ds,
epochs=5,
validation_data=test_classifier_ds,
)
# Unfreeze the BERT model for fine-tuning
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,
)
Model: "classification"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ┃ 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
<keras.src.callbacks.history.History at 0x7a0e5fd9bf50>
创建端到端模型并评估
当您要部署模型时，最好是模型已经包含其预处理管道，这样您就不必在生产环境中重新实现预处理逻辑。让我们创建一个端到端模型，该模型将 TextVectorization 层合并到 evaluate 方法中，并进行评估。我们将传递原始字符串作为输入。
# 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]