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/RuPERTa-base/README.md

README.md

@@ -0,0 +1,125 @@
+---
+language: es
+thumbnail: https://i.imgur.com/DUlT077.jpg
+widget:
+- text: "España es un país muy <mask> en la UE"
+---
+
+# RuPERTa: the Spanish RoBERTa 🎃<img src="https://abs-0.twimg.com/emoji/v2/svg/1f1ea-1f1f8.svg" alt="spain flag" width="25"/>
+
+RuPERTa-base (uncased) is a [RoBERTa model](https://github.com/pytorch/fairseq/tree/master/examples/roberta) trained on a *uncased* verison of [big Spanish corpus](https://github.com/josecannete/spanish-corpora).
+RoBERTa iterates on BERT's pretraining procedure, including training the model longer, with bigger batches over more data; removing the next sentence prediction objective; training on longer sequences; and dynamically changing the masking pattern applied to the training data.
+The architecture is the same as `roberta-base`:
+
+`roberta.base:` **RoBERTa** using the **BERT-base architecture 125M** params
+
+## Benchmarks 🧾 
+WIP (I continue working on it) 🚧
+
+| Task/Dataset     |    F1 | Precision | Recall |                                                                        Fine-tuned model |                                                                                                                                                                                                                                                                                               Reproduce it |
+| -------- | ----: | --------: | -----: | --------------------------------------------------------------------------------------: | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| POS      | 97.39 |     97.47 |  97.32 | [RuPERTa-base-finetuned-pos](https://huggingface.co/mrm8488/RuPERTa-base-finetuned-pos) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mrm8488/shared_colab_notebooks/blob/master/RuPERTa_base_finetuned_POS.ipynb)
+| NER      | 77.55 |     75.53 |  79.68 | [RuPERTa-base-finetuned-ner](https://huggingface.co/mrm8488/RuPERTa-base-finetuned-ner) |
+| SQUAD-es v1 |  to-do |       |    |[RuPERTa-base-finetuned-squadv1](https://huggingface.co/mrm8488/RuPERTa-base-finetuned-squadv1)
+| SQUAD-es v2 |  to-do |       |  |[RuPERTa-base-finetuned-squadv2](https://huggingface.co/mrm8488/RuPERTa-base-finetuned-squadv2)
+
+## Model in action 🔨
+
+### Usage for POS and NER 🏷
+
+```python
+import torch
+from transformers import AutoModelForTokenClassification, AutoTokenizer
+
+id2label = {
+    "0": "B-LOC",
+    "1": "B-MISC",
+    "2": "B-ORG",
+    "3": "B-PER",
+    "4": "I-LOC",
+    "5": "I-MISC",
+    "6": "I-ORG",
+    "7": "I-PER",
+    "8": "O"
+}
+
+tokenizer = AutoTokenizer.from_pretrained('mrm8488/RuPERTa-base-finetuned-ner')
+model = AutoModelForTokenClassification.from_pretrained('mrm8488/RuPERTa-base-finetuned-ner')
+
+text ="Julien, CEO de HF, nació en Francia."
+
+input_ids = torch.tensor(tokenizer.encode(text)).unsqueeze(0)
+
+outputs = model(input_ids)
+last_hidden_states = outputs[0]
+
+for m in last_hidden_states:
+  for index, n in enumerate(m):
+    if(index > 0 and index <= len(text.split(" "))):
+      print(text.split(" ")[index-1] + ": " + id2label[str(torch.argmax(n).item())])
+
+# Output:
+'''
+Julien,: I-PER
+CEO: O
+de: O
+HF,: B-ORG
+nació: I-PER
+en: I-PER
+Francia.: I-LOC
+'''
+```
+
+For **POS** just change the `id2label` dictionary and the model path to [mrm8488/RuPERTa-base-finetuned-pos](https://huggingface.co/mrm8488/RuPERTa-base-finetuned-pos)
+
+### Fast usage for LM with `pipelines` 🧪
+
+```python
+from transformers import AutoModelWithLMHead, AutoTokenizer
+model = AutoModelWithLMHead.from_pretrained('mrm8488/RuPERTa-base')
+tokenizer = AutoTokenizer.from_pretrained("mrm8488/RuPERTa-base", do_lower_case=True)
+
+from transformers import pipeline
+
+pipeline_fill_mask = pipeline("fill-mask", model=model, tokenizer=tokenizer)
+
+pipeline_fill_mask("España es un país muy <mask> en la UE")
+```
+
+```json
+[
+  {
+    "score": 0.1814306527376175,
+    "sequence": "<s> españa es un país muy importante en la ue</s>",
+    "token": 1560
+  },
+  {
+    "score": 0.024842597544193268,
+    "sequence": "<s> españa es un país muy fuerte en la ue</s>",
+    "token": 2854
+  },
+  {
+    "score": 0.02473250962793827,
+    "sequence": "<s> españa es un país muy pequeño en la ue</s>",
+    "token": 2948
+  },
+  {
+    "score": 0.023991240188479424,
+    "sequence": "<s> españa es un país muy antiguo en la ue</s>",
+    "token": 5240
+  },
+  {
+    "score": 0.0215945765376091,
+    "sequence": "<s> españa es un país muy popular en la ue</s>",
+    "token": 5782
+  }
+]
+```
+
+## Acknowledgments
+
+I thank [🤗/transformers team](https://github.com/huggingface/transformers) for answering my doubts and Google for helping me with the [TensorFlow Research Cloud](https://www.tensorflow.org/tfrc) program.
+
+> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488)
+
+> Made with <span style="color: #e25555;">&hearts;</span> in Spain