Training issue of a Transformer based Encoder-Decoder model based on pre-trained BanglaBERT
I just tried to train an EncoderDeoder model for **summarization** task based on pre-trained *[BanglaBERT](https://huggingface.co/csebuetnlp/banglabert)*, which is an **ELECTRA** discriminator model pre-trained with the Replaced Token Detection (RTD) objective. Surprisingly, after spending 4500 steps on 10k training data, the model wasn't trained a bit since the **ROUGE-2** scores were just **0.0000**. To make sure I used that 4500-checkpoint to generate summaries for testing purposes; it generated a fixed-length (50) output (even if I change the test input of different lengths) containing the **[CLS]** token **49** times and a **[SEP]** token **lastly**. Basically, I followed the [Warm-starting encoder-decoder models with 🤗Transformers](https://colab.research.google.com/drive/1Ekd5pUeCX7VOrMx94_czTkwNtLN32Uyu?usp=sharing) notebook. Can anybody give any clue what could be the issue here? Thanks in advance.

In my case,

Tokenization **BanglaBERT** model:
```
tokenizer = AutoTokenizer.from_pretrained("csebuetnlp/banglabert")
tokenizer.bos_token = tokenizer.cls_token
tokenizer.eos_token = tokenizer.sep_token
```

Input pre-processing function:
```
def process_data_to_model_inputs(batch):
    inputs = tokenizer(batch['text'], padding="max_length", truncation=True, max_length=encoder_max_length)
    outputs = tokenizer(batch['summary'], padding="max_length", truncation=True, max_length=decoder_max_length)

    batch["input_ids"] = inputs.input_ids
    batch["attention_mask"] = inputs.attention_mask
    batch["decoder_input_ids"] = outputs.input_ids
    batch["decoder_attention_mask"] = outputs.attention_mask
    batch["labels"] = outputs.input_ids.copy()

    batch["labels"] = [[-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]]
    return batch
```

Mapping the pre-processing function to the batches of examples:
```
train_data = train_data.map(
    process_data_to_model_inputs, 
    batched=True,
    batch_size=batch_size,
    remove_columns=["text", "summary"]
)
train_data.set_format(
    type="torch", columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"],
)

valid_data = valid_data.map(
    process_data_to_model_inputs, 
    batched=True, 
    batch_size=batch_size,
    remove_columns=["text", "summary"]
)
valid_data.set_format(
    type="torch", columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"],
)
```

**BanglaBERT** model and its config settings:
```
bert2bert = EncoderDecoderModel.from_encoder_decoder_pretrained("csebuetnlp/banglabert", "csebuetnlp/banglabert")
bert2bert.config.decoder_start_token_id = tokenizer.bos_token_id
bert2bert.config.eos_token_id = tokenizer.eos_token_id
bert2bert.config.pad_token_id = tokenizer.pad_token_id

bert2bert.config.vocab_size = bert2bert.config.decoder.vocab_size
bert2bert.config.max_length = 128
bert2bert.config.min_length = 42
bert2bert.config.early_stopping = True
bert2bert.config.length_penalty = 2.0
bert2bert.config.num_beams = 8
bert2bert.config.remove_invalid_values = True
bert2bert.config.repetition_penalty = 2.0
bert2bert.config.length_penalty = 2.0
```
I used the **Seq2SeqTrainer** for training. The **Seq2SeqTrainingArguments** were as follows:

```
    evaluation_strategy = "steps",
    per_device_train_batch_size = batch_size,
    per_device_eval_batch_size = batch_size,
    predict_with_generate = True,
    logging_steps = 1000, 
    save_steps = 500, 
    eval_steps = 5000, 
    warmup_steps = 500,
    overwrite_output_dir = True,
    save_total_limit = 2,
    num_train_epochs = 20,
    fp16 = True
```
I am literally getting the same issue, invested hours trying to figure out what is wrong but still did not get it. I tried both BERTurk and mBERT and both of them gave the same issue and even training loss goes near 0 instantly I am not sure what to do.
<|||||>Sorry for being late. Yes, that's because what approach you are following will work in the specific version more precisely, v4.2.1 but not in the current release (maybe 4.18).<|||||>> Sorry for being late. Yes, that's because what approach you are following will work in the specific version more precisely, v4.2.1 but not in the current release (maybe 4.18).

Hii sorry but I am not sure what exactly to change can you be more specific? Should I not use Encoder Decoder class or should I change the trainer ? I am new to this! 

Thank you in advance. <|||||>I believe he means to ensure your version of transformers and other relevant libraries match the example <|||||>>I believe he means to ensure your version of transformers and other relevant libraries match the example

@AniketRajpoot  exactly this is and the adjustments needed for version 4.18 are in progress. I have opened another [issue](https://github.com/huggingface/blog/issues/292#issue-1228916923). you can keep your eyes if you want.<|||||>Thank you so much @AbuUbaida @RaedShabbir, I understand the issue! 
