SciNCL
SciNCL is a pre-trained BERT language model to generate document-level embeddings of research papers.
It uses the citation graph neighborhood to generate samples for contrastive learning.
Prior to the contrastive training, the model is initialized with weights from scibert-scivocab-uncased.
The underlying citation embeddings are trained on the S2ORC citation graph.
Paper: Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings (EMNLP 2022 paper).制作ai的软件
Code: https://github.com/malteos/scincl即梦下载官方
PubMedNCL: Working with biomedical papers? Try PubMedNCL.快问ai
How to use the pretrained model
from Transformersai是什么东西? import AutoTokenizer, AutoModel
# load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained('malteos/scincl')
model = AutoModel.from_pretrained('malteos/scincl')
papers = [{'title': 'BERT', 'abstract': 'We introduce a new language representation model called BERT'},
{'title': 'Attention is all you need', 'abstract': ' The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'}]
# concatenate title and abstract with [SEP] token
title_abs = [d['title'] + tokenizer.sep_token + (d.get('abstract') or '') for d in papers]
# preprocess the input
inputs = tokenizer(title_abs, padding=True, truncation=True, return_tensors="pt", max_length=512)
# inference
result = model(**inputs)
# take the first token ([CLS] token) in the batch as the embedding
embeddings = result.last_hidden_state[:, 0, :]
Triplet Mining Parameters
| Setting猫箱下载安装 | Value即梦al |
|---|---|
| seed | 4 |
| triples_per_query | 5 |
| easy_positives_count | 5 |
| easy_positives_strategy | 5 |
| easy_positives_k | 20-25 |
| easy_negatives_count | 3 |
| easy_negatives_strategy | random_without_knn |
| hard_negatives_count | 2 |
| hard_negatives_strategy | knn |
| hard_negatives_k | 3998-4000 |
These model weights are the ones that yielded the best results on SciDocs (seed=4).
In the paper we report the SciDocs results as mean over ten seeds.
| model制作ai的软件 | mag-f1ai是什么东西? | mesh-f1元宝大模型 | co-view-map元宝大模型 | co-view-ndcg做al视频怎么赚钱 | co-read-map元宝大模型 | co-read-ndcg制作ai的软件 | cite-map下载官方即梦a1 | cite-ndcg即梦al | cocite-mapal一键脱装入口 | cocite-ndcgima是什么软件 | recomm-ndcg人工智能ai哪个好 | recomm-P@1快问ai | Avg即梦下载官方 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 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 |
| 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 |
| 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 |
| 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 |
| SPECTER | 82 | 86.4 | 83.6 | 91.5 | 84.5 | 92.4 | 88.3 | 94.9 | 88.1 | 94.8 | 53.9 | 20 | 80 |
| 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 |
| SciNCL (seed=4)快问ai | 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 |
Additional evaluations are available in the paper.猫箱下载安装
License
MIT百度流畅ai制作
数据统计
数据评估
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