Model reference · open weights
unsup-simcse-bert-uncased is an open-weight embedding model from princeton-nlp. 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 | princeton-nlp |
|---|---|
| Type | Embedding models |
| Task | Embeddings |
| Context | 512 tokens |
| Runs with | transformers |
| Released | 2022-03-02 |
| Popularity | 1k downloads / month |
| Licence | Unknown |
About
More information needed
This model can be used for the task of Feature Engineering.
More information needed
The model should not be used to intentionally create hostile or alienating environments for people.
Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
The model craters note in the Github Repository
We train unsupervised SimCSE on 106 randomly sampled sentences from English Wikipedia, and train supervised SimCSE on the combination of MNLI and SNLI datasets (314k).
More information needed
More information needed
The model craters note in the associated paper
Our evaluation code for sentence embeddings is based on a modified version of SentEval. It evaluates sentence embeddings on semantic textual similarity (STS) tasks and downstream transfer tasks. For STS tasks, our evaluation takes the "all" setting, and report Spearman's correlation. See associated paper (Appendix B) for evaluation details.
More information needed
More information needed
More information needed
The model craters note in the associated paper
Uniformity and alignment. We also observe that (1) though pre-trained embeddings have good alignment, their uniformity is poor (i.e., the embeddings are highly anisotropic); (2) post-processing methods like BERT-flow and BERT-whitening greatly improve uniformity but also suffer a degeneration in alignment; (3) unsupervised SimCSE effectively improves uniformity of pre-trained embeddings whereas keeping a good alignment;(4) incorporating supervised data in SimCSE further amends alignment.
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
More information needed
More information needed
More information needed
More information needed
BibTeX:
@inproceedings{gao2021simcse,
title={{SimCSE}: Simple Contrastive Learning of Sentence Embeddings},
author={Gao, Tianyu and Yao, Xingcheng and Chen, Danqi},
booktitle={Empirical Methods in Natural Language Processing (EMNLP)},
year={2021}
}
More information needed
More information needed
Princeton NLP group in collaboration with Ezi Ozoani and the Hugging Face team
If you have any questions related to the code or the paper, feel free to email Tianyu (tianyug@cs.princeton.edu) and Xingcheng (yxc18@mails.tsinghua.edu.cn). If you encounter any problems when using the code, or want to report a bug, you can open an issue. Please try to specify the problem with details so we can help you better and quicker!
Use the code below to get started with the model.
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("princeton-nlp/unsup-simcse-bert-base-uncased")
model = AutoModel.from_pretrained("princeton-nlp/unsup-simcse-bert-base-uncased")
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys unsup-simcse-bert-uncased for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (unsup-simcse-bert-uncased 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":"unsup-simcse-bert-uncased","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.