Model reference · open weights
scincl is an open-weight embedding model from malteos. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Released by | malteos |
|---|---|
| Type | Embedding models |
| Task | Embeddings |
| Parameters (lead) | 110M |
| Context | 512 tokens |
| Runs with | sentence-transformers |
| Released | 2022-03-02 |
| Popularity | 27k downloads / month |
| Licence | Open weights |
About
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.
Code: https://github.com/malteos/scincl
PubMedNCL: Working with biomedical papers? Try PubMedNCL.
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer("malteos/scincl")
# Concatenate the title and abstract with the [SEP] token
papers = [
"BERT [SEP] We introduce a new language representation model called BERT",
"Attention is all you need [SEP] The dominant sequence transduction models are based on complex recurrent or convolutional neural networks",
]
# Inference
embeddings = model.encode(papers)
# Compute the (cosine) similarity between embeddings
similarity = model.similarity(embeddings[0], embeddings[1])
print(similarity.item())
# => 0.8440517783164978
from transformers 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, :]
# calculate the similarity
embeddings = torch.nn.functional.normalize(embeddings, p=2, dim=1)
similarity = (embeddings[0] @ embeddings[1].T)
print(similarity.item())
# => 0.8440518379211426
| Setting | Value |
|---|---|
| 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 | 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 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) | 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.
MIT
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys scincl for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (scincl below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/embeddings \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"scincl","input":"text to embed"}'
Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.