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--- |
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datasets: |
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- squad_v1 |
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license: mit |
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--- |
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# LONGFORMER-BASE-4096 fine-tuned on SQuAD v1 |
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This is longformer-base-4096 model fine-tuned on SQuAD v1 dataset for question answering task. |
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[Longformer](https://arxiv.org/abs/2004.05150) model created by Iz Beltagy, Matthew E. Peters, Arman Coha from AllenAI. As the paper explains it |
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> `Longformer` is a BERT-like model for long documents. |
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The pre-trained model can handle sequences with upto 4096 tokens. |
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## Model Training |
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This model was trained on google colab v100 GPU. You can find the fine-tuning colab here [](https://colab.research.google.com/drive/1zEl5D-DdkBKva-DdreVOmN0hrAfzKG1o?usp=sharing). |
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Few things to keep in mind while training longformer for QA task, |
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by default longformer uses sliding-window local attention on all tokens. But For QA, all question tokens should have global attention. For more details on this please refer the paper. The `LongformerForQuestionAnswering` model automatically does that for you. To allow it to do that |
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1. The input sequence must have three sep tokens, i.e the sequence should be encoded like this |
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` <s> question</s></s> context</s>`. If you encode the question and answer as a input pair, then the tokenizer already takes care of that, you shouldn't worry about it. |
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2. `input_ids` should always be a batch of examples. |
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## Results |
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|Metric | # Value | |
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|-------------|---------| |
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| Exact Match | 85.1466 | |
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| F1 | 91.5415 | |
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## Model in Action 馃殌 |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForQuestionAnswering, |
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tokenizer = AutoTokenizer.from_pretrained("valhalla/longformer-base-4096-finetuned-squadv1") |
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model = AutoModelForQuestionAnswering.from_pretrained("valhalla/longformer-base-4096-finetuned-squadv1") |
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text = "Huggingface has democratized NLP. Huge thanks to Huggingface for this." |
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question = "What has Huggingface done ?" |
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encoding = tokenizer(question, text, return_tensors="pt") |
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input_ids = encoding["input_ids"] |
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# default is local attention everywhere |
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# the forward method will automatically set global attention on question tokens |
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attention_mask = encoding["attention_mask"] |
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start_scores, end_scores = model(input_ids, attention_mask=attention_mask) |
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all_tokens = tokenizer.convert_ids_to_tokens(input_ids[0].tolist()) |
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answer_tokens = all_tokens[torch.argmax(start_scores) :torch.argmax(end_scores)+1] |
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answer = tokenizer.decode(tokenizer.convert_tokens_to_ids(answer_tokens)) |
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# output => democratized NLP |
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``` |
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The `LongformerForQuestionAnswering` isn't yet supported in `pipeline` . I'll update this card once the support has been added. |
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> Created with 鉂わ笍 by Suraj Patil [](https://github.com/patil-suraj/) |
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[](https://twitter.com/psuraj28) |
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