# How to get answer with RobertaForQuestionAnswering

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**Category:** Models\
**Created:** [October 26, 2021, 1:47am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fhow-to-get-answer-with-robertaforquestionanswering%2F11080 "2021-10-26T01:47:49Z")\
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**Author:** ![seunghon](https://avatars.discourse-cdn.com/v4/letter/s/e274bd/32.png) [@seunghon](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2Fseunghon)%5C
**Post date:** [October 26, 2021, 1:47am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fhow-to-get-answer-with-robertaforquestionanswering%2F11080%2F1 "2021-10-26T01:47:49Z")

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Dear list,

What I like to do is to pretrain a model and finetune it for Q&A with Squad dataset. I trained a model as a roberta after reading the blog on huggingface site.

And I would like to make it as a Q&A model, I tried to do finetuning following this document: [Fine-tuning with custom datasets — transformers 4.11.3 documentation](https://huggingface.co/transformers/custom_datasets.html#qa-squad). ( I changed Distilbert to Roberta to match the pretrained model and fine tuning training)

As a result, I’ve got a fine tuned model and want to test on it. So, I loaded model as described in this document: [RoBERTa — transformers 4.11.3 documentation](https://huggingface.co/transformers/model_doc/roberta.html#robertaforquestionanswering)

However, the output doesn’t show the ‘answer’ that I am looking for. Could you tell me how I can get the answer? I’ve searched the code but I couldn’t find it.

Thank you in advance.

Best regards,  
Seungho

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<div class="post-metadata">

**Author:** ![nielsr](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/nielsr/32/39617_2.png) [@nielsr](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2Fnielsr)%5C
**Post date:** [October 26, 2021, 9:30am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fhow-to-get-answer-with-robertaforquestionanswering%2F11080%2F2 "2021-10-26T09:30:59Z")

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Let’s take an example with an already-finetuned RoBERTa model from the hub.

```auto
from transformers import RobertaTokenizer, RobertaForQuestionAnswering

model_name = "deepset/roberta-base-squad2"
tokenizer = RobertaTokenizer.from_pretrained(model_name)
model = RobertaForQuestionAnswering.from_pretrained(model_name)

question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
encoding = tokenizer(question, text, return_tensors="pt")

# forward pass
outputs = model(**encoding)

predicted_start_idx = outputs.start_logits.argmax(-1).item()
predicted_end_idx = outputs.end_logits.argmax(-1).item()

# decode
predicted_answer = tokenizer.decode(encoding.input_ids.squeeze()[predicted_start_idx : predicted_end_idx + 1])
print(predicted_answer)

```

xxxForQuestionAnswering models output `start_logits` and `end_logits`, indicating which token the model thinks is at the start of the answer, and which token is at the end of the answer. Both are of shape `(batch_size, sequence_length)`. To get the highest score (logit), we do an argmax on the last dimension . Next, we use the `decode` method of the tokenizer to turn the predicted token IDs back to text.