问使用TFDebertaForSequenceClassification进行二进制分类时，错误形状并不不兼容。

EN

 ValueError: Shapes (32, 2) and (32, 1) are incompatible

with tf.device('/cpu:0'):
  train_data = tf.data.Dataset.from_tensor_slices((train_df["body"].values, train_df["label"].values))
  valid_data = tf.data.Dataset.from_tensor_slices((valid_df.body.values, valid_df.label.values))

def map_example_to_dict(input_ids, attention_masks, token_type_ids, label):
  X = {
      "input_ids": input_ids,
      "token_type_ids": token_type_ids,
      "attention_mask": attention_masks,
  }

  Y = label
  return X, Y

def encode_examples(df, limit=-1):

  # prepare list, so that we can build up final TensorFlow dataset from slices.
  input_ids_list = []
  token_type_ids_list = []
  attention_mask_list = []
  labels = []
    
  for data in df.to_numpy():

    bert_input = tokenizer(data[2],add_special_tokens=True,
            max_length=MAX_SEQ_LEN,
            padding='max_length',
            return_token_type_ids=True,
            truncation=True)
  
    input_ids_list.append(bert_input['input_ids'])
    token_type_ids_list.append(bert_input['token_type_ids'])
    attention_mask_list.append(bert_input['attention_mask'])
    labels.append([data[3]])

  return tf.data.Dataset.from_tensor_slices((input_ids_list, attention_mask_list, token_type_ids_list, labels)).map(map_example_to_dict)

encoded_train_input = encode_examples(train_df).shuffle(1000).batch(32, drop_remainder=True)
encoded_valid_input = encode_examples(valid_df).shuffle(1000).batch(32, drop_remainder=True)

model = TFDebertaForSequenceClassification.from_pretrained('kamalkraj/deberta-base')
lr = 2e-6 #1e-6 #2e-5 #3e-5
epochs = 1
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=lr), 
              loss=tf.keras.losses.BinaryCrossentropy(),
              metrics=[tf.keras.metrics.BinaryAccuracy(), tf.keras.metrics.Recall()])
history = model.fit(encoded_train_input, validation_data=encoded_valid_input, epochs=epochs, verbose=1)