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/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es/README.md

README.md

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+---
+language: es
+thumbnail: https://i.imgur.com/jgBdimh.png
+---
+
+# BETO (Spanish BERT) + Spanish SQuAD2.0 + distillation using 'bert-base-multilingual-cased' as teacher
+
+This model is a fine-tuned on [SQuAD-es-v2.0](https://github.com/ccasimiro88/TranslateAlignRetrieve) and **distilled** version of [BETO](https://github.com/dccuchile/beto) for **Q&A**.
+
+Distillation makes the model **smaller, faster, cheaper and lighter** than [bert-base-spanish-wwm-cased-finetuned-spa-squad2-es](https://github.com/huggingface/transformers/blob/master/model_cards/mrm8488/bert-base-spanish-wwm-cased-finetuned-spa-squad2-es/README.md)
+
+This model was fine-tuned on the same dataset but using **distillation** during the process as mentioned above (and one more train epoch).
+
+The **teacher model** for the distillation was `bert-base-multilingual-cased`. It is the same teacher used for `distilbert-base-multilingual-cased` AKA [**DistilmBERT**](https://github.com/huggingface/transformers/tree/master/examples/distillation) (on average is twice as fast as **mBERT-base**).
+
+## Details of the downstream task (Q&A) - Dataset
+
+<details>
+
+[SQuAD-es-v2.0](https://github.com/ccasimiro88/TranslateAlignRetrieve)
+
+| Dataset                 | # Q&A |
+| ----------------------- | ----- |
+| SQuAD2.0 Train          | 130 K |
+| SQuAD2.0-es-v2.0        | 111 K |
+| SQuAD2.0 Dev            | 12 K  |
+| SQuAD-es-v2.0-small Dev | 69 K  |
+
+</details>
+
+## Model training
+
+The model was trained on a Tesla P100 GPU and 25GB of RAM with the following command:
+
+```bash
+!export SQUAD_DIR=/path/to/squad-v2_spanish \
+&& python transformers/examples/distillation/run_squad_w_distillation.py \
+  --model_type bert \
+  --model_name_or_path dccuchile/bert-base-spanish-wwm-cased \
+  --teacher_type bert \
+  --teacher_name_or_path bert-base-multilingual-cased \
+  --do_train \
+  --do_eval \
+  --do_lower_case \
+  --train_file $SQUAD_DIR/train-v2.json \
+  --predict_file $SQUAD_DIR/dev-v2.json \
+  --per_gpu_train_batch_size 12 \
+  --learning_rate 3e-5 \
+  --num_train_epochs 5.0 \
+  --max_seq_length 384 \
+  --doc_stride 128 \
+  --output_dir /content/model_output \
+  --save_steps 5000 \
+  --threads 4 \
+  --version_2_with_negative
+```
+
+## Results:
+
+| Metric    | # Value     |
+| --------- | ----------- |
+| **Exact** | **90.77**48 |
+| **F1**    | **94.94**71 |
+
+```json
+{
+  "exact": 90.77483309730933,
+  "f1": 94.94714391266254,
+  "total": 69202,
+  "HasAns_exact": 86.60850599781898,
+  "HasAns_f1": 92.90582885592328,
+  "HasAns_total": 45850,
+  "NoAns_exact": 98.95512161699212,
+  "NoAns_f1": 98.95512161699212,
+  "NoAns_total": 23352,
+  "best_exact": 90.77483309730933,
+  "best_exact_thresh": 0.0,
+  "best_f1": 94.94714391266305,
+  "best_f1_thresh": 0.0
+}
+```
+
+## Comparison:
+
+|                              Model                              | f1 score  |
+| :-------------------------------------------------------------: | :-------: |
+|       bert-base-spanish-wwm-cased-finetuned-spa-squad2-es       |   86.07   |
+| **distill**-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es | **94.94** |
+
+So, yes, this version is even more accurate.
+
+### Model in action
+
+Fast usage with **pipelines**:
+
+```python
+from transformers import *
+
+# Important!: By now the QA pipeline is not compatible with fast tokenizer, but they are working on it. So that pass the object to the tokenizer {"use_fast": False} as in the following example:
+
+nlp = pipeline(
+    'question-answering', 
+    model='mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es',
+    tokenizer=(
+        'mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es',  
+        {"use_fast": False}
+    )
+)
+
+nlp(
+    {
+        'question': '¿Para qué lenguaje está trabajando?',
+        'context': 'Manuel Romero está colaborando activamente con huggingface/transformers ' +
+                    'para traer el poder de las últimas técnicas de procesamiento de lenguaje natural al idioma español'
+    }
+)
+# Output: {'answer': 'español', 'end': 169, 'score': 0.67530957344621, 'start': 163}
+```
+
+Play with this model and ```pipelines``` in a Colab:
+
+<a href="https://colab.research.google.com/github/mrm8488/shared_colab_notebooks/blob/master/Using_Spanish_BERT_fine_tuned_for_Q%26A_pipelines.ipynb" target="_parent"><img src="https://camo.githubusercontent.com/52feade06f2fecbf006889a904d221e6a730c194/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg"></a>
+
+<details>
+
+1.  Set the context and ask some questions:
+
+![Set context and questions](https://media.giphy.com/media/mCIaBpfN0LQcuzkA2F/giphy.gif)
+
+2.  Run predictions:
+
+![Run the model](https://media.giphy.com/media/WT453aptcbCP7hxWTZ/giphy.gif)
+</details>
+
+More about ``` Huggingface pipelines```? check this Colab out:
+
+<a href="https://colab.research.google.com/github/mrm8488/shared_colab_notebooks/blob/master/Huggingface_pipelines_demo.ipynb" target="_parent"><img src="https://camo.githubusercontent.com/52feade06f2fecbf006889a904d221e6a730c194/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg"></a>
+
+> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488)
+
+> Made with <span style="color: #e25555;">&hearts;</span> in Spain