Migrate model card from transformers-repo

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Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/mrm8488/mobilebert-uncased-finetuned-squadv1/README.md

README.md

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+---
+language: en
+datasets:
+- squad
+---
+
+# MobileBERT + SQuAD (v1.1) 📱❓
+
+[mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) fine-tuned on [SQUAD v2.0 dataset](https://rajpurkar.github.io/SQuAD-explorer/explore/v2.0/dev/) for **Q&A** downstream task.
+
+## Details of the downstream task (Q&A) - Model 🧠
+
+**MobileBERT** is a thin version of *BERT_LARGE*, while equipped with bottleneck structures and a carefully designed balance between self-attentions and feed-forward networks.
+
+The checkpoint used here is the original MobileBert Optimized Uncased English: (uncased_L-24_H-128_B-512_A-4_F-4_OPT) checkpoint.
+
+More about the model [here](https://arxiv.org/abs/2004.02984)
+
+## Details of the downstream task (Q&A) - Dataset 📚
+
+**S**tanford **Q**uestion **A**nswering **D**ataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.
+SQuAD v1.1 contains **100,000+** question-answer pairs on **500+** articles.
+
+## Model training 🏋️‍
+
+The model was trained on a Tesla P100 GPU and 25GB of RAM with the following command:
+
+```bash
+python transformers/examples/question-answering/run_squad.py \
+  --model_type bert \
+  --model_name_or_path 'google/mobilebert-uncased' \
+  --do_eval \
+  --do_train \
+  --do_lower_case \
+  --train_file '/content/dataset/train-v1.1.json' \
+  --predict_file '/content/dataset/dev-v1.1.json' \
+  --per_gpu_train_batch_size 16 \
+  --learning_rate 3e-5 \
+  --num_train_epochs 5 \
+  --max_seq_length 384 \
+  --doc_stride 128 \
+  --output_dir '/content/output' \
+  --overwrite_output_dir \
+  --save_steps 1000
+```
+
+It is important to say that this models converges much faster than other ones. So, it is also cheap to fine-tune.
+
+## Test set Results 🧾
+
+| Metric | # Value   |
+| ------ | --------- |
+| **EM** | **82.33** |
+| **F1** | **89.64** |
+| **Size**| **94 MB** |
+
+### Model in action 🚀
+
+Fast usage with **pipelines**:
+
+```python
+from transformers import pipeline
+QnA_pipeline = pipeline('question-answering', model='mrm8488/mobilebert-uncased-finetuned-squadv1')
+QnA_pipeline({
+    'context': 'A new strain of flu that has the potential to become a pandemic has been identified in China by scientists.',
+    'question': 'Who did identified it ?'
+    })
+    
+# Output: {'answer': 'scientists.', 'end': 106, 'score': 0.7885545492172241, 'start': 96}
+```
+
+> 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