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+# SciBERT-NLI
+
+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].
+
+The model uses the original `scivocab` wordpiece vocabulary and was trained using the **average pooling strategy** and a **softmax loss**. 
+
+**Base model**: `allenai/scibert-scivocab-cased` from HuggingFace AutoModel
+
+**Parameters**:
+
+| Parameter      | Value |
+|----------------|-------|
+| Batch size     | 64    |
+| Training steps | 20000 |
+| Warmup steps   | 1450  |
+
+**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       |
+|-----------------------------|-------------|
+| `scibert-nli` (ours)        | 74.50       |
+| `bert-base-nli-mean-tokens` | 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] I. Beltagy et al, [SciBERT: A Pretrained Language Model for Scientific Text](https://www.aclweb.org/anthology/D19-1371/)
+
+[2] A. Conneau et al., [Supervised Learning of Universal Sentence Representations from Natural Language Inference Data](https://www.aclweb.org/anthology/D17-1070/)