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--- |
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tags: |
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- feature-extraction |
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language: en |
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datasets: |
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- SciDocs |
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- s2orc |
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metrics: |
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- F1 |
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- accuracy |
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- map |
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- ndcg |
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license: mit |
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--- |
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## SciNCL |
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SciNCL is a pre-trained BERT language model to generate document-level embeddings of research papers. |
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It uses the citation graph neighborhood to generate samples for contrastive learning. |
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Prior to the contrastive training, the model is initialized with weights from [scibert-scivocab-uncased](https://huggingface.co/allenai/scibert_scivocab_uncased). |
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The underlying citation embeddings are trained on the [S2ORC citation graph](https://github.com/allenai/s2orc). |
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Paper: [Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings (EMNLP 2022 paper)](https://arxiv.org/abs/2202.06671). |
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Code: https://github.com/malteos/scincl |
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PubMedNCL: Working with biomedical papers? Try [PubMedNCL](https://huggingface.co/malteos/PubMedNCL). |
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## How to use the pretrained model |
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```python |
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from transformers import AutoTokenizer, AutoModel |
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# load model and tokenizer |
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tokenizer = AutoTokenizer.from_pretrained('malteos/scincl') |
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model = AutoModel.from_pretrained('malteos/scincl') |
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papers = [{'title': 'BERT', 'abstract': 'We introduce a new language representation model called BERT'}, |
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{'title': 'Attention is all you need', 'abstract': ' The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'}] |
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# concatenate title and abstract with [SEP] token |
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title_abs = [d['title'] + tokenizer.sep_token + (d.get('abstract') or '') for d in papers] |
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# preprocess the input |
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inputs = tokenizer(title_abs, padding=True, truncation=True, return_tensors="pt", max_length=512) |
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# inference |
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result = model(**inputs) |
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# take the first token ([CLS] token) in the batch as the embedding |
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embeddings = result.last_hidden_state[:, 0, :] |
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``` |
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## Triplet Mining Parameters |
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| **Setting** | **Value** | |
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|-------------------------|--------------------| |
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| seed | 4 | |
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| triples_per_query | 5 | |
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| easy_positives_count | 5 | |
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| easy_positives_strategy | 5 | |
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| easy_positives_k | 20-25 | |
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| easy_negatives_count | 3 | |
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| easy_negatives_strategy | random_without_knn | |
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| hard_negatives_count | 2 | |
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| hard_negatives_strategy | knn | |
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| hard_negatives_k | 3998-4000 | |
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## SciDocs Results |
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These model weights are the ones that yielded the best results on SciDocs (`seed=4`). |
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In the paper we report the SciDocs results as mean over ten seeds. |
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| **model** | **mag-f1** | **mesh-f1** | **co-view-map** | **co-view-ndcg** | **co-read-map** | **co-read-ndcg** | **cite-map** | **cite-ndcg** | **cocite-map** | **cocite-ndcg** | **recomm-ndcg** | **recomm-P@1** | **Avg** | |
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|-------------------|-----------:|------------:|----------------:|-----------------:|----------------:|-----------------:|-------------:|--------------:|---------------:|----------------:|----------------:|---------------:|--------:| |
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| Doc2Vec | 66.2 | 69.2 | 67.8 | 82.9 | 64.9 | 81.6 | 65.3 | 82.2 | 67.1 | 83.4 | 51.7 | 16.9 | 66.6 | |
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| fasttext-sum | 78.1 | 84.1 | 76.5 | 87.9 | 75.3 | 87.4 | 74.6 | 88.1 | 77.8 | 89.6 | 52.5 | 18 | 74.1 | |
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| SGC | 76.8 | 82.7 | 77.2 | 88 | 75.7 | 87.5 | 91.6 | 96.2 | 84.1 | 92.5 | 52.7 | 18.2 | 76.9 | |
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| SciBERT | 79.7 | 80.7 | 50.7 | 73.1 | 47.7 | 71.1 | 48.3 | 71.7 | 49.7 | 72.6 | 52.1 | 17.9 | 59.6 | |
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| SPECTER | 82 | 86.4 | 83.6 | 91.5 | 84.5 | 92.4 | 88.3 | 94.9 | 88.1 | 94.8 | 53.9 | 20 | 80 | |
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| SciNCL (10 seeds) | 81.4 | 88.7 | 85.3 | 92.3 | 87.5 | 93.9 | 93.6 | 97.3 | 91.6 | 96.4 | 53.9 | 19.3 | 81.8 | |
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| **SciNCL (seed=4)** | 81.2 | 89.0 | 85.3 | 92.2 | 87.7 | 94.0 | 93.6 | 97.4 | 91.7 | 96.5 | 54.3 | 19.6 | 81.9 | |
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Additional evaluations are available in the paper. |
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## License |
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MIT |
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