ValueError: You have to specify either decoder_input_ids or decoder_inputs_embeds

🤗Transformers
1

I have used many ways to create the dataset and feed it to the model but everytime I am getting the same error , I am new to hugging face , Can anyone please help me how to train a TFAutoModelForSeq2SeqLM model please
In my dataset both Input and Output are sentences
I want a model that mimic a person.


from tensorflow.keras.optimizers import Adam
from tensorflow.keras.losses import SparseCategoricalCrossentropy

import tensorflow as tf
# Load model directly
from transformers import AutoTokenizer, TFAutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
model = TFAutoModelForSeq2SeqLM.from_pretrained("facebook/blenderbot-400M-distill")
from datasets import load_dataset

dataset = load_dataset("csv", data_files="/content/conversation_data.csv", sep=",",split='train')
max_input_length = 1024
max_target_length = 128

def preprocess_function(examples):
    model_inputs = tokenizer(examples["Input"], max_length=max_input_length, truncation=True)

    # Setup the tokenizer for targets
    with tokenizer.as_target_tokenizer():
        labels = tokenizer(examples["Output"], max_length=max_target_length, truncation=True)

    model_inputs["labels"] = labels["input_ids"]
    return model_inputs

tokenized_datasets = dataset.map(
    preprocess_function, batched=True, remove_columns=["Input", "Output"]
)

# Dataset({
 #   features: ['input_ids', 'attention_mask', 'labels'],
  #  num_rows: 2304
#})
from transformers import AutoTokenizer, DataCollatorWithPadding
data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="tf")

tf_train_dataset = tokenized_datasets.to_tf_dataset(
    columns=["attention_mask", "input_ids"],
    label_cols=["labels"],
    shuffle=True,
    collate_fn=data_collator,
    batch_size=8,
)

# Compile and train the model
model.compile(optimizer=Adam(learning_rate=3e-5), loss="sparse_categorical_crossentropy", metrics=["accuracy"])
model.fit(tf_train_dataset)


    ValueError: Exception encountered when calling layer 'model' (type TFBlenderbotMainLayer).

ValueError: You have to specify either decoder_input_ids or decoder_inputs_embeds

        Call arguments received by layer 'decoder' (type TFBlenderbotDecoder):
          • input_ids=None
          • inputs_embeds=None
          • attention_mask=None
          • position_ids=None
          • encoder_hidden_states=tf.Tensor(shape=(None, None, 1280), dtype=float32)
          • encoder_attention_mask=tf.Tensor(shape=(None, None), dtype=int32)
          • head_mask=None
          • cross_attn_head_mask=None
          • past_key_values=None
          • use_cache=True
          • output_attentions=False
          • output_hidden_states=False
          • return_dict=True
          • training=True
    
    
    Call arguments received by layer 'model' (type TFBlenderbotMainLayer):
      • input_ids=tf.Tensor(shape=(None, None), dtype=int32)
      • attention_mask=tf.Tensor(shape=(None, None), dtype=int32)
      • decoder_input_ids=None
      • decoder_attention_mask=None
      • decoder_position_ids=None
      • head_mask=None
      • decoder_head_mask=None
      • cross_attn_head_mask=None
      • encoder_outputs=None
      • past_key_values=None
      • inputs_embeds=None
      • decoder_inputs_embeds=None
      • use_cache=True
      • output_attentions=False
      • output_hidden_states=False
      • return_dict=True
      • training=True
      • kwargs=<class 'inspect._empty'>


Call arguments received by layer 'tf_blenderbot_for_conditional_generation' (type TFBlenderbotForConditionalGeneration):
  • input_ids={'input_ids': 'tf.Tensor(shape=(None, None), dtype=int64)', 'attention_mask': 'tf.Tensor(shape=(None, None), dtype=int64)'}
  • attention_mask=None
  • decoder_input_ids=None
  • decoder_attention_mask=None
  • decoder_position_ids=None
  • head_mask=None
  • decoder_head_mask=None
  • cross_attn_head_mask=None
  • encoder_outputs=None
  • past_key_values=None
  • inputs_embeds=None
  • decoder_inputs_embeds=None
  • use_cache=None
  • output_attentions=None
  • output_hidden_states=None
  • return_dict=None
  • labels=None
  • training=True