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/gsarti/biobert-nli/README.md

README.md

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+# BioBERT-NLI
+
+This is the model [BioBERT](https://github.com/dmis-lab/biobert) [1] fine-tuned on the [SNLI](https://nlp.stanford.edu/projects/snli/) and the [MultiNLI](https://www.nyu.edu/projects/bowman/multinli/) datasets using the [`sentence-transformers` library](https://github.com/UKPLab/sentence-transformers/) to produce universal sentence embeddings [2].
+
+The model uses the original BERT wordpiece vocabulary and was trained using the **average pooling strategy** and a **softmax loss**.
+
+**Base model**: `monologg/biobert_v1.1_pubmed` from HuggingFace's `AutoModel`.
+
+**Training time**: ~6 hours on the NVIDIA Tesla P100 GPU provided in Kaggle Notebooks.
+
+**Parameters**:
+
+| Parameter        | Value |
+|------------------|-------|
+| Batch size       | 64    |
+| Training steps   | 30000 |
+| Warmup steps     | 1450  |
+| Lowercasing      | False |
+| Max. Seq. Length | 128   |
+
+**Performances**: The performance was evaluated on the test portion of the [STS dataset](http://ixa2.si.ehu.es/stswiki/index.php/STSbenchmark) using Spearman rank correlation and compared to the performances of a general BERT base model obtained with the same procedure to verify their similarity.
+
+| Model                         | Score       |
+|-------------------------------|-------------|
+| `biobert-nli` (this)          | 73.40       |
+| `gsarti/scibert-nli`          | 74.50       |
+| `bert-base-nli-mean-tokens`[3]| 77.12       |
+
+An example usage for similarity-based scientific paper retrieval is provided in the [Covid Papers Browser](https://github.com/gsarti/covid-papers-browser) repository.
+
+**References:**
+
+[1] J. Lee et al, [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://academic.oup.com/bioinformatics/article/36/4/1234/5566506)
+
+[2] A. Conneau et al., [Supervised Learning of Universal Sentence Representations from Natural Language Inference Data](https://www.aclweb.org/anthology/D17-1070/)
+
+[3] N. Reimers et I. Gurevych, [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://www.aclweb.org/anthology/D19-1410/)