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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 (Arxiv preprint)](https://arxiv.org/abs/2202.06671).
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Code: https://github.com/malteos/scincl
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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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