Migrate model card from transformers-repo

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/mrm8488/longformer-base-4096-finetuned-squadv2/README.md

README.md

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+---
+language: en
+datasets:
+- squad_v2
+---
+
+# Longformer-base-4096 fine-tuned on SQuAD v2
+
+[Longformer-base-4096 model](https://huggingface.co/allenai/longformer-base-4096) fine-tuned on [SQuAD v2](https://rajpurkar.github.io/SQuAD-explorer/) for **Q&A** downstream task.
+
+## Longformer-base-4096
+
+[Longformer](https://arxiv.org/abs/2004.05150) is a transformer model for long documents. 
+
+`longformer-base-4096` is a BERT-like model started from the RoBERTa checkpoint and pretrained for MLM on long documents. It supports sequences of length up to 4,096. 
+ 
+Longformer uses a combination of a sliding window (local) attention and global attention. Global attention is user-configured based on the task to allow the model to learn task-specific representations.
+
+## Details of the downstream task (Q&A) - Dataset 📚 🧐 ❓
+
+Dataset ID: ```squad_v2``` from  [HugginFace/NLP](https://github.com/huggingface/nlp)
+| Dataset  | Split | # samples |
+| -------- | ----- | --------- |
+| squad_v2 | train | 130319      |
+| squad_v2 | valid  | 11873     |
+
+How to load it from [nlp](https://github.com/huggingface/nlp)
+
+```python
+train_dataset  = nlp.load_dataset('squad_v2', split=nlp.Split.TRAIN)
+valid_dataset = nlp.load_dataset('squad_v2', split=nlp.Split.VALIDATION)
+```
+Check out more about this dataset and others in [NLP Viewer](https://huggingface.co/nlp/viewer/)
+
+
+## Model fine-tuning 🏋️‍
+
+The training script is a slightly modified version of [this one](https://colab.research.google.com/drive/1zEl5D-DdkBKva-DdreVOmN0hrAfzKG1o?usp=sharing)
+
+
+
+## Model in Action 🚀
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForQuestionAnswering
+
+tokenizer = AutoTokenizer.from_pretrained("mrm8488/longformer-base-4096-finetuned-squadv2")
+model = AutoModelForQuestionAnswering.from_pretrained("mrm8488/longformer-base-4096-finetuned-squadv2")
+
+text = "Huggingface has democratized NLP. Huge thanks to Huggingface for this."
+question = "What has Huggingface done ?"
+encoding = tokenizer(question, text, return_tensors="pt")
+input_ids = encoding["input_ids"]
+
+# default is local attention everywhere
+# the forward method will automatically set global attention on question tokens
+attention_mask = encoding["attention_mask"]
+
+start_scores, end_scores = model(input_ids, attention_mask=attention_mask)
+all_tokens = tokenizer.convert_ids_to_tokens(input_ids[0].tolist())
+
+answer_tokens = all_tokens[torch.argmax(start_scores) :torch.argmax(end_scores)+1]
+answer = tokenizer.decode(tokenizer.convert_tokens_to_ids(answer_tokens))
+
+# output => democratized NLP
+```
+If given the same context we ask something that is not there, the output for **no answer** will be ```<s>```
+
+> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)
+
+> Made with <span style="color: #e25555;">&hearts;</span> in Spain