_batch_encode_plus() hat ein unerwartetes Schlüsselwortargument „return_attention_masks“ erhalten. ⇐ Python

[Anonymous]

Ich untersuche das RoBERTA-Modell zur Erkennung von Emotionen in Tweets.
Auf Google Colab. Folgen Sie dieser Noteboook-Datei von Kaggle – https://www.kaggle.com/ishivinal/tweet- ... d=38608295
Code-Snippet:

Code: Select all

def regular_encode(texts, tokenizer, maxlen=512):
enc_di = tokenizer.batch_encode_plus(
texts,
return_attention_masks=True,
return_token_type_ids=False,
pad_to_max_length=True,
#padding=True,
max_length=maxlen
)

return np.array(enc_di['input_ids'])

def build_model(transformer, max_len=160):
input_word_ids = Input(shape=(max_len,), dtype=tf.int32, name="input_word_ids")
sequence_output = transformer(input_word_ids)[0]
cls_token = sequence_output[:, 0, :]
out = Dense(13, activation='softmax')(cls_token)

model = Model(inputs=input_word_ids, outputs=out)
model.compile(Adam(lr=1e-5), loss='categorical_crossentropy', metrics=['accuracy'])

return model

AUTO = tf.data.experimental.AUTOTUNE
MODEL = 'roberta-base'
tokenizer = AutoTokenizer.from_pretrained(MODEL)

X_train_t = regular_encode(X_train, tokenizer, maxlen= max_len)
X_test_t = regular_encode(X_test, tokenizer, maxlen=max_len)

Im Teil „regular_encode“ erhalte ich die folgende Fehlermeldung:

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TypeError                                 Traceback (most recent call last)
 in ()
----> 1 X_train_t = regular_encode(X_train, tokenizer, maxlen= max_len)
2 X_test_t = regular_encode(X_test, tokenizer, maxlen=max_len)

2 frames
/usr/local/lib/python3.7/dist-packages/transformers/models/gpt2/tokenization_gpt2_fast.py in _batch_encode_plus(self, *args, **kwargs)
161         )
162
--> 163         return super()._batch_encode_plus(*args, **kwargs)
164
165     def _encode_plus(self, *args, **kwargs) -> BatchEncoding:

TypeError: _batch_encode_plus() got an unexpected keyword argument 'return_attention_masks'