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Migrate model card from transformers-repo

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Read announcement at https://discuss.huggingface.co/t/announcement-all-model-cards-will-be-migrated-to-hf-co-model-repos/2755
Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/valhalla/bart-large-finetuned-squadv1/README.md

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+ ---
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+ datasets:
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+ - squad
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+ ---
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+
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+ # BART-LARGE finetuned on SQuADv1
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+
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+ This is bart-large model finetuned on SQuADv1 dataset for question answering task
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+
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+ ## Model details
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+ BART was propsed in the [paper](https://arxiv.org/abs/1910.13461) **BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension**.
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+ BART is a seq2seq model intended for both NLG and NLU tasks.
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+
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+ To use BART for question answering tasks, we feed the complete document into the encoder and decoder, and use the top
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+ hidden state of the decoder as a representation for each
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+ word. This representation is used to classify the token. As given in the paper bart-large achives comparable to ROBERTa on SQuAD.
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+ Another notable thing about BART is that it can handle sequences with upto 1024 tokens.
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+
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+ | Param | #Value |
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+ |---------------------|--------|
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+ | encoder layers | 12 |
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+ | decoder layers | 12 |
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+ | hidden size | 4096 |
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+ | num attetion heads | 16 |
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+ | on disk size | 1.63GB |
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+
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+
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+ ## Model training
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+ This model was trained on google colab v100 GPU.
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+ You can find the fine-tuning colab here
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+ [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1I5cK1M_0dLaf5xoewh6swcm5nAInfwHy?usp=sharing).
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+
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+
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+ ## Results
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+ The results are actually slightly worse than given in the paper.
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+ In the paper the authors mentioned that bart-large achieves 88.8 EM and 94.6 F1
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+
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+ | Metric | #Value |
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+ |--------|--------|
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+ | EM | 86.8022|
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+ | F1 | 92.7342|
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+
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+
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+ ## Model in Action 🚀
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+ ```python3
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+ from transformers import BartTokenizer, BartForQuestionAnswering
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+ import torch
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+
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+ tokenizer = BartTokenizer.from_pretrained('valhalla/bart-large-finetuned-squadv1')
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+ model = BartForQuestionAnswering.from_pretrained('valhalla/bart-large-finetuned-squadv1')
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+
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+ question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
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+ encoding = tokenizer(question, text, return_tensors='pt')
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+ input_ids = encoding['input_ids']
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+ attention_mask = encoding['attention_mask']
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+
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+ start_scores, end_scores = model(input_ids, attention_mask=attention_mask, output_attentions=False)[:2]
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+
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+ all_tokens = tokenizer.convert_ids_to_tokens(input_ids[0])
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+ answer = ' '.join(all_tokens[torch.argmax(start_scores) : torch.argmax(end_scores)+1])
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+ answer = tokenizer.convert_tokens_to_ids(answer.split())
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+ answer = tokenizer.decode(answer)
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+ #answer => 'a nice puppet'
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+ ```
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+
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+ > Created with ❤️ by Suraj Patil [![Github icon](https://cdn0.iconfinder.com/data/icons/octicons/1024/mark-github-32.png)](https://github.com/patil-suraj/)
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+ [![Twitter icon](https://cdn0.iconfinder.com/data/icons/shift-logotypes/32/Twitter-32.png)](https://twitter.com/psuraj28)