julien-c
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Migrate model card from transformers-repo

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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/scibert-nli/README.md

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+ # SciBERT-NLI
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+ This is the model [SciBERT](https://github.com/allenai/scibert) [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].
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+ The model uses the original `scivocab` wordpiece vocabulary and was trained using the **average pooling strategy** and a **softmax loss**.
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+ **Base model**: `allenai/scibert-scivocab-cased` from HuggingFace's `AutoModel`.
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+ **Training time**: ~4 hours on the NVIDIA Tesla P100 GPU provided in Kaggle Notebooks.
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+ **Parameters**:
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+ | Parameter | Value |
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+ |------------------|-------|
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+ | Batch size | 64 |
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+ | Training steps | 20000 |
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+ | Warmup steps | 1450 |
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+ | Lowercasing | True |
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+ | Max. Seq. Length | 128 |
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+ **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.
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+ | Model | Score |
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+ |-------------------------------|-------------|
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+ | `scibert-nli` (this) | 74.50 |
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+ | `bert-base-nli-mean-tokens`[3]| 77.12 |
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+ An example usage for similarity-based scientific paper retrieval is provided in the [Covid Papers Browser](https://github.com/gsarti/covid-papers-browser) repository.
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+ **References:**
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+ [1] I. Beltagy et al, [SciBERT: A Pretrained Language Model for Scientific Text](https://www.aclweb.org/anthology/D19-1371/)
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+ [2] A. Conneau et al., [Supervised Learning of Universal Sentence Representations from Natural Language Inference Data](https://www.aclweb.org/anthology/D17-1070/)
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+ [3] N. Reimers et I. Gurevych, [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://www.aclweb.org/anthology/D19-1410/)