[model card] model card for bart-large-finetuned-squadv1 (#4977)

* [model card] model card for bart-large-finetuned-squadv1

* add metadata link to the dataset

model_cards/valhalla/bart-large-finetuned-squadv1/README.md

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