# gsarti /scibert-nli

 # 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's AutoModel. **Training time**: ~4 hours on the NVIDIA Tesla P100 GPU provided in Kaggle Notebooks. **Parameters**: | Parameter | Value | |------------------|-------| | Batch size | 64 | | Training steps | 20000 | | 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. | Model | Score | |-------------------------------|-------------| | scibert-nli (this) | 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] 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/) [3] N. Reimers et I. Gurevych, [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://www.aclweb.org/anthology/D19-1410/)