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].
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**.
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
**Base model**: `allenai/scibert-scivocab-cased` from HuggingFace's `AutoModel`.
**Training time**: ~4 hours on the NVIDIA Tesla P100 GPU provided in Kaggle Notebooks.
**Parameters**:
**Parameters**:
| Parameter | Value |
| Parameter | Value |
|----------------|-------|
|------------------|-------|
| Batch size | 64 |
| Batch size | 64 |
| Training steps | 20000 |
| Training steps | 20000 |
| Warmup steps | 1450 |
| Warmup steps | 1450 |
| Lowercasing | True |
| 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.
**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 |
| Model | Score |
|-------------------------------|-------------|
|-----------------------------|-------------|
| `scibert-nli` (this) | 74.50 |
| `scibert-nli` (ours) | 74.50 |
| `bert-base-nli-mean-tokens`[3]| 77.12 |
| `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.
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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@@ -30,3 +32,5 @@ An example usage for similarity-based scientific paper retrieval is provided in
[1] I. Beltagy et al, [SciBERT: A Pretrained Language Model for Scientific Text](https://www.aclweb.org/anthology/D19-1371/)
[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/)
[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/)
