How to train encoder decoder for explicit negation generation
Hi
I am trying to generate negations out of non-negated sentences.
I used a simple “I have tea” => “I don’t have tea” formatted dataset for training an XLMR encoder-decoder model using the example provided in the collab.

```
# set special tokens

roberta_shared.config.decoder_start_token_id = tokenizer.bos_token_id                                             

roberta_shared.config.eos_token_id = tokenizer.eos_token_id

# sensible parameters for beam search

# set decoding params                               

roberta_shared.config.max_length = 64

roberta_shared.config.early_stopping = True

roberta_shared.config.no_repeat_ngram_size = 3

roberta_shared.config.length_penalty = 2.0

roberta_shared.config.num_beams = 4

roberta_shared.config.vocab_size = roberta_shared.config.encoder.vocab_size

```

But the test set produces different tokens than the source. How can I preserve the source tokens when generating the output.
[“I have it.”, “I love tea”, “I can have coffee.”] =>
[‘I have no it.’, “I’ll not love.” “I can’t have food.”]
Where the model modifies the words in the sentence.


Hello, thanks for opening an issue! We try to keep the github issues for bugs/feature requests.
Could you ask your question on the [forum](https://discusss.huggingface.co) instead?

Thanks!