Keras 2 : examples : NLP – BERT による意味的類似性 (翻訳/解説)

翻訳 : (株)クラスキャット セールスインフォメーション
作成日時 : 05/29/2022 (keras 2.9.0)

* 本ページは、Keras の以下のドキュメントを翻訳した上で適宜、補足説明したものです：

• Code examples : Natural Language Processing : Semantic Similarity with BERT embeddings (Author: Mohamad Merchant)

* サンプルコードの動作確認はしておりますが、必要な場合には適宜、追加改変しています。
* ご自由にリンクを張って頂いてかまいませんが、sales-info@classcat.com までご一報いただけると嬉しいです。

 

 

Keras 2 : examples : 自然言語処理 – BERT による意味的類似性

Description : BERT モデルの SNLI コーパス上の最調整による自然言語推論。

 

イントロダクション

意味的類似性は 2 つのセンテンスが、それが意味するものの観点から、どの程度類似しているかを決定するタスクです。このサンプルは Transformers でセンテンスの意味的類似性を予測するために SNLI (Stanford 自然言語推論) コーパスの使用方法を実演します。BERT モデルを最調整します、これは入力として 2 つのセンテンスを取り、これらの 2 つのセンテンスの類似性スコアを出力します。

 

References

• BERT
• SNLI

 

セットアップ

Note : pip install transformers (version >= 2.11.0) により HuggingFace transformers をインストールします。

import numpy as np
import pandas as pd
import tensorflow as tf
import transformers

 

Configuration

max_length = 128  # Maximum length of input sentence to the model.
batch_size = 32
epochs = 2

# Labels in our dataset.
labels = ["contradiction", "entailment", "neutral"]

 

データのロード

!curl -LO https://raw.githubusercontent.com/MohamadMerchant/SNLI/master/data.tar.gz
!tar -xvzf data.tar.gz

# There are more than 550k samples in total; we will use 100k for this example.
train_df = pd.read_csv("SNLI_Corpus/snli_1.0_train.csv", nrows=100000)
valid_df = pd.read_csv("SNLI_Corpus/snli_1.0_dev.csv")
test_df = pd.read_csv("SNLI_Corpus/snli_1.0_test.csv")

# Shape of the data
print(f"Total train samples : {train_df.shape[0]}")
print(f"Total validation samples: {valid_df.shape[0]}")
print(f"Total test samples: {valid_df.shape[0]}")

  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100 11.1M  100 11.1M    0     0  5231k      0  0:00:02  0:00:02 --:--:-- 5231k
SNLI_Corpus/
SNLI_Corpus/snli_1.0_dev.csv
SNLI_Corpus/snli_1.0_train.csv
SNLI_Corpus/snli_1.0_test.csv

Total train samples : 100000
Total validation samples: 10000
Total test samples: 10000

データセット概要 :

• sentence1 : ペアの作者に供給された前提となるキャプション。
  

• sentence2 : ペアの作者により書かれた仮説のキャプション。
  

• similarity : これはアノテーターの大部分により選択されたラベルです。マジョリティが存在しない場合には、ラベル “-” が使用されます (ここではそのようなサンプルはスキップします)。

ここにデータセットの “similarity” ラベル値があります :

• Contradiction: センテンスは類似性を持ちません。
  

• Entailment: センテンスは類似の意味を持ちます。
  

• Neutral: センテンスは中立的です。

データセットから 1 つのサンプルを見てみましょう :

print(f"Sentence1: {train_df.loc[1, 'sentence1']}")
print(f"Sentence2: {train_df.loc[1, 'sentence2']}")
print(f"Similarity: {train_df.loc[1, 'similarity']}")

Sentence1: A person on a horse jumps over a broken down airplane.
Sentence2: A person is at a diner, ordering an omelette.
Similarity: contradiction

 

前処理

# We have some NaN entries in our train data, we will simply drop them.
print("Number of missing values")
print(train_df.isnull().sum())
train_df.dropna(axis=0, inplace=True)

Number of missing values
similarity    0
sentence1     0
sentence2     3
dtype: int64

訓練ターゲットの分布。

print("Train Target Distribution")
print(train_df.similarity.value_counts())

Train Target Distribution
entailment       33384
contradiction    33310
neutral          33193
-                  110
Name: similarity, dtype: int64

検証ターゲットの分布。

print("Validation Target Distribution")
print(valid_df.similarity.value_counts())

Validation Target Distribution
entailment       3329
contradiction    3278
neutral          3235
-                 158
Name: similarity, dtype: int64

値 “-” は訓練と検証ターゲットの一部として現れます。これらのサンプルはスキップします。

train_df = (
    train_df[train_df.similarity != "-"]
    .sample(frac=1.0, random_state=42)
    .reset_index(drop=True)
)
valid_df = (
    valid_df[valid_df.similarity != "-"]
    .sample(frac=1.0, random_state=42)
    .reset_index(drop=True)
)

one-hot エンコード訓練、検証とテストラベル。

train_df["label"] = train_df["similarity"].apply(
    lambda x: 0 if x == "contradiction" else 1 if x == "entailment" else 2
)
y_train = tf.keras.utils.to_categorical(train_df.label, num_classes=3)

valid_df["label"] = valid_df["similarity"].apply(
    lambda x: 0 if x == "contradiction" else 1 if x == "entailment" else 2
)
y_val = tf.keras.utils.to_categorical(valid_df.label, num_classes=3)

test_df["label"] = test_df["similarity"].apply(
    lambda x: 0 if x == "contradiction" else 1 if x == "entailment" else 2
)
y_test = tf.keras.utils.to_categorical(test_df.label, num_classes=3)

 

カスタムデータ generator の作成

class BertSemanticDataGenerator(tf.keras.utils.Sequence):
    """Generates batches of data.

    Args:
        sentence_pairs: Array of premise and hypothesis input sentences.
        labels: Array of labels.
        batch_size: Integer batch size.
        shuffle: boolean, whether to shuffle the data.
        include_targets: boolean, whether to incude the labels.

    Returns:
        Tuples `([input_ids, attention_mask, `token_type_ids], labels)`
        (or just `[input_ids, attention_mask, `token_type_ids]`
         if `include_targets=False`)
    """

    def __init__(
        self,
        sentence_pairs,
        labels,
        batch_size=batch_size,
        shuffle=True,
        include_targets=True,
    ):
        self.sentence_pairs = sentence_pairs
        self.labels = labels
        self.shuffle = shuffle
        self.batch_size = batch_size
        self.include_targets = include_targets
        # Load our BERT Tokenizer to encode the text.
        # We will use base-base-uncased pretrained model.
        self.tokenizer = transformers.BertTokenizer.from_pretrained(
            "bert-base-uncased", do_lower_case=True
        )
        self.indexes = np.arange(len(self.sentence_pairs))
        self.on_epoch_end()

    def __len__(self):
        # Denotes the number of batches per epoch.
        return len(self.sentence_pairs) // self.batch_size

    def __getitem__(self, idx):
        # Retrieves the batch of index.
        indexes = self.indexes[idx * self.batch_size : (idx + 1) * self.batch_size]
        sentence_pairs = self.sentence_pairs[indexes]

        # With BERT tokenizer's batch_encode_plus batch of both the sentences are
        # encoded together and separated by [SEP] token.
        encoded = self.tokenizer.batch_encode_plus(
            sentence_pairs.tolist(),
            add_special_tokens=True,
            max_length=max_length,
            return_attention_mask=True,
            return_token_type_ids=True,
            pad_to_max_length=True,
            return_tensors="tf",
        )

        # Convert batch of encoded features to numpy array.
        input_ids = np.array(encoded["input_ids"], dtype="int32")
        attention_masks = np.array(encoded["attention_mask"], dtype="int32")
        token_type_ids = np.array(encoded["token_type_ids"], dtype="int32")

        # Set to true if data generator is used for training/validation.
        if self.include_targets:
            labels = np.array(self.labels[indexes], dtype="int32")
            return [input_ids, attention_masks, token_type_ids], labels
        else:
            return [input_ids, attention_masks, token_type_ids]

    def on_epoch_end(self):
        # Shuffle indexes after each epoch if shuffle is set to True.
        if self.shuffle:
            np.random.RandomState(42).shuffle(self.indexes)

 

モデルの構築

# Create the model under a distribution strategy scope.
strategy = tf.distribute.MirroredStrategy()

with strategy.scope():
    # Encoded token ids from BERT tokenizer.
    input_ids = tf.keras.layers.Input(
        shape=(max_length,), dtype=tf.int32, name="input_ids"
    )
    # Attention masks indicates to the model which tokens should be attended to.
    attention_masks = tf.keras.layers.Input(
        shape=(max_length,), dtype=tf.int32, name="attention_masks"
    )
    # Token type ids are binary masks identifying different sequences in the model.
    token_type_ids = tf.keras.layers.Input(
        shape=(max_length,), dtype=tf.int32, name="token_type_ids"
    )
    # Loading pretrained BERT model.
    bert_model = transformers.TFBertModel.from_pretrained("bert-base-uncased")
    # Freeze the BERT model to reuse the pretrained features without modifying them.
    bert_model.trainable = False

    bert_output = bert_model(
        input_ids, attention_mask=attention_masks, token_type_ids=token_type_ids
    )
    sequence_output = bert_output.last_hidden_state
    pooled_output = bert_output.pooler_output
    # Add trainable layers on top of frozen layers to adapt the pretrained features on the new data.
    bi_lstm = tf.keras.layers.Bidirectional(
        tf.keras.layers.LSTM(64, return_sequences=True)
    )(sequence_output)
    # Applying hybrid pooling approach to bi_lstm sequence output.
    avg_pool = tf.keras.layers.GlobalAveragePooling1D()(bi_lstm)
    max_pool = tf.keras.layers.GlobalMaxPooling1D()(bi_lstm)
    concat = tf.keras.layers.concatenate([avg_pool, max_pool])
    dropout = tf.keras.layers.Dropout(0.3)(concat)
    output = tf.keras.layers.Dense(3, activation="softmax")(dropout)
    model = tf.keras.models.Model(
        inputs=[input_ids, attention_masks, token_type_ids], outputs=output
    )

    model.compile(
        optimizer=tf.keras.optimizers.Adam(),
        loss="categorical_crossentropy",
        metrics=["acc"],
    )


print(f"Strategy: {strategy}")
model.summary()

HBox(children=(FloatProgress(value=0.0, description='Downloading', max=433.0, style=ProgressStyle(description_…

HBox(children=(FloatProgress(value=0.0, description='Downloading', max=536063208.0, style=ProgressStyle(descri…

Strategy: <tensorflow.python.distribute.mirrored_strategy.MirroredStrategy object at 0x7faf9dc63a90>
Model: "functional_1"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_ids (InputLayer)          [(None, 128)]        0                                            
__________________________________________________________________________________________________
attention_masks (InputLayer)    [(None, 128)]        0                                            
__________________________________________________________________________________________________
token_type_ids (InputLayer)     [(None, 128)]        0                                            
__________________________________________________________________________________________________
tf_bert_model (TFBertModel)     ((None, 128, 768), ( 109482240   input_ids[0][0]                  
                                                                 attention_masks[0][0]            
                                                                 token_type_ids[0][0]             
__________________________________________________________________________________________________
bidirectional (Bidirectional)   (None, 128, 128)     426496      tf_bert_model[0][0]              
__________________________________________________________________________________________________
global_average_pooling1d (Globa (None, 128)          0           bidirectional[0][0]              
__________________________________________________________________________________________________
global_max_pooling1d (GlobalMax (None, 128)          0           bidirectional[0][0]              
__________________________________________________________________________________________________
concatenate (Concatenate)       (None, 256)          0           global_average_pooling1d[0][0]   
                                                                 global_max_pooling1d[0][0]       
__________________________________________________________________________________________________
dropout_37 (Dropout)            (None, 256)          0           concatenate[0][0]                
__________________________________________________________________________________________________
dense (Dense)                   (None, 3)            771         dropout_37[0][0]                 
==================================================================================================
Total params: 109,909,507
Trainable params: 427,267
Non-trainable params: 109,482,240
_______________________________________________________________________________________

訓練と検証データ generator を作成します。

train_data = BertSemanticDataGenerator(
    train_df[["sentence1", "sentence2"]].values.astype("str"),
    y_train,
    batch_size=batch_size,
    shuffle=True,
)
valid_data = BertSemanticDataGenerator(
    valid_df[["sentence1", "sentence2"]].values.astype("str"),
    y_val,
    batch_size=batch_size,
    shuffle=False,
)

HBox(children=(FloatProgress(value=0.0, description='Downloading', max=231508.0, style=ProgressStyle(descripti…

 

モデルの訓練

訓練は「特徴抽出」を実行するトップ層に対してのみ行われ、これはモデルが事前訓練済みモデルの表現を使用することを可能にします。

history = model.fit(
    train_data,
    validation_data=valid_data,
    epochs=epochs,
    use_multiprocessing=True,
    workers=-1,
)

Epoch 1/2
3121/3121 [==============================] - 666s 213ms/step - loss: 0.6925 - acc: 0.7049 - val_loss: 0.5294 - val_acc: 0.7899
Epoch 2/2
3121/3121 [==============================] - 661s 212ms/step - loss: 0.5917 - acc: 0.7587 - val_loss: 0.4955 - val_acc: 0.8052

 

最調整

このステップは、特徴抽出モデルが新しいデータで収束するまで訓練された後でのみ、実行されなければなりません。

これはオプションの最後のステップで、bert_model は解凍されて非常に低い学習率で再訓練されます。新しいデータに事前訓練済みの特徴を段階的に適応させることによりこれは意味のある改良をもたらすことができます。

# Unfreeze the bert_model.
bert_model.trainable = True
# Recompile the model to make the change effective.
model.compile(
    optimizer=tf.keras.optimizers.Adam(1e-5),
    loss="categorical_crossentropy",
    metrics=["accuracy"],
)
model.summary()

Model: "functional_1"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_ids (InputLayer)          [(None, 128)]        0                                            
__________________________________________________________________________________________________
attention_masks (InputLayer)    [(None, 128)]        0                                            
__________________________________________________________________________________________________
token_type_ids (InputLayer)     [(None, 128)]        0                                            
__________________________________________________________________________________________________
tf_bert_model (TFBertModel)     ((None, 128, 768), ( 109482240   input_ids[0][0]                  
                                                                 attention_masks[0][0]            
                                                                 token_type_ids[0][0]             
__________________________________________________________________________________________________
bidirectional (Bidirectional)   (None, 128, 128)     426496      tf_bert_model[0][0]              
__________________________________________________________________________________________________
global_average_pooling1d (Globa (None, 128)          0           bidirectional[0][0]              
__________________________________________________________________________________________________
global_max_pooling1d (GlobalMax (None, 128)          0           bidirectional[0][0]              
__________________________________________________________________________________________________
concatenate (Concatenate)       (None, 256)          0           global_average_pooling1d[0][0]   
                                                                 global_max_pooling1d[0][0]       
__________________________________________________________________________________________________
dropout_37 (Dropout)            (None, 256)          0           concatenate[0][0]                
__________________________________________________________________________________________________
dense (Dense)                   (None, 3)            771         dropout_37[0][0]                 
==================================================================================================
Total params: 109,909,507
Trainable params: 109,909,507
Non-trainable params: 0
_______________________________________________________________________________________

 

モデル全体を end-to-end に訓練する

history = model.fit(
    train_data,
    validation_data=valid_data,
    epochs=epochs,
    use_multiprocessing=True,
    workers=-1,
)

Epoch 1/2
3121/3121 [==============================] - 1574s 504ms/step - loss: 0.4698 - accuracy: 0.8181 - val_loss: 0.3787 - val_accuracy: 0.8598
Epoch 2/2
3121/3121 [==============================] - 1569s 503ms/step - loss: 0.3516 - accuracy: 0.8702 - val_loss: 0.3416 - val_accuracy: 0.8757

 

テストセットでのモデルの評価

test_data = BertSemanticDataGenerator(
    test_df[["sentence1", "sentence2"]].values.astype("str"),
    y_test,
    batch_size=batch_size,
    shuffle=False,
)
model.evaluate(test_data, verbose=1)

312/312 [==============================] - 55s 177ms/step - loss: 0.3697 - accuracy: 0.8629

[0.3696725070476532, 0.8628805875778198]

 

カスタムセンテンス上の推論

def check_similarity(sentence1, sentence2):
    sentence_pairs = np.array([[str(sentence1), str(sentence2)]])
    test_data = BertSemanticDataGenerator(
        sentence_pairs, labels=None, batch_size=1, shuffle=False, include_targets=False,
    )

    proba = model.predict(test_data[0])[0]
    idx = np.argmax(proba)
    proba = f"{proba[idx]: .2f}%"
    pred = labels[idx]
    return pred, proba

幾つかのサンプルのセンテンスペアで結果を確認します。

sentence1 = "Two women are observing something together."
sentence2 = "Two women are standing with their eyes closed."
check_similarity(sentence1, sentence2)

('contradiction', ' 0.91%')

sentence1 = "A smiling costumed woman is holding an umbrella"
sentence2 = "A happy woman in a fairy costume holds an umbrella"
check_similarity(sentence1, sentence2)

('neutral', ' 0.88%')

sentence1 = "A soccer game with multiple males playing"
sentence2 = "Some men are playing a sport"
check_similarity(sentence1, sentence2)

('entailment', ' 0.94%')

以上