prajjwal1_bert-mini
prajjwal1 · View on Hugging Face ↗
Model card
The complete upstream card, rendered from this payload's README.md — the same hash-verified bytes the torrent distributes. Images and off-site links are removed; the original card on Hugging Face carries them.
language:
- en
license:
- mit
tags:
- BERT
- MNLI
- NLI
- transformer
- pre-training
The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the official Google BERT repository.
This is one of the smaller pre-trained BERT variants, together with bert-small and bert-medium. They were introduced in the study Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (arxiv), and ported to HF for the study Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics (arXiv). These models are supposed to be trained on a downstream task.
If you use the model, please consider citing both the papers:
@misc{bhargava2021generalization,
title={Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics},
author={Prajjwal Bhargava and Aleksandr Drozd and Anna Rogers},
year={2021},
eprint={2110.01518},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@article{DBLP:journals/corr/abs-1908-08962,
author = {Iulia Turc and
Ming{-}Wei Chang and
Kenton Lee and
Kristina Toutanova},
title = {Well-Read Students Learn Better: The Impact of Student Initialization
on Knowledge Distillation},
journal = {CoRR},
volume = {abs/1908.08962},
year = {2019},
url = {http://arxiv.org/abs/1908.08962},
eprinttype = {arXiv},
eprint = {1908.08962},
timestamp = {Thu, 29 Aug 2019 16:32:34 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1908-08962.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Config of this model: prajjwal1/bert-mini (L=4, H=256) Model Link
Other models to check out:
prajjwal1/bert-tiny(L=2, H=128) Model Linkprajjwal1/bert-small(L=4, H=512) Model Linkprajjwal1/bert-medium(L=8, H=512) Model Link
Original Implementation and more info can be found in this Github repository.
Twitter: @prajjwal_1
Magnet link
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magnet:?xt=urn:btih:16c52ddfff325424679a93cafac4551e76e00dbc&dn=prajjwal1_bert-miniOpen magnet in torrent client · infohash 16c52ddfff325424679a93cafac4551e76e00dbc
Files & hashes
| Path | Size | sha1 | sha256 |
|---|---|---|---|
| README.md | 2.4 KB (2,501 B) | d002216a55c7e53a0b708b82a1b41930e2c87650 | 87ac33ecba7f6d8d493a93ff81b43f3d16a00609817e442e354fb551928227bd |
| config.json | 286 B (286 B) | 20d87df26f7e77863454545e6402be9ee3a67b94 | d32ac9faf7e47097bea0395fd2e0cc8afc9ce038ad7fa41bdffa37386972b524 |
| pytorch_model.bin | 43.0 MB (45,106,985 B) | 108e9af1a86049505011d82181f035ba16d8d82c | f7902e759e678cf77852a40a710e79bb83acb475c44c177773246d610818a5db |
| vocab.txt | 226.1 KB (231,508 B) | fb140275c155a9c7c5a3b3e0e77a9e839594a938 | 07eced375cec144d27c900241f3e339478dec958f92fddbc551f295c992038a3 |
Cite this release
- Canonical URL
- https://aiseedbank.org/models/prajjwal1_bert-mini/
- Slug
- prajjwal1_bert-mini
- Infohash
- 16c52ddfff325424679a93cafac4551e76e00dbc
- License
- mit
- Signing key fingerprint
- 85a3b32c3712427b
Every file carries a locally computed sha256 — verify a download against the signed sums: prajjwal1_bert-mini.SHA256SUMS (+ minisign signature).
Provenance
| Upstream repository | prajjwal1/bert-mini |
|---|---|
| Revision (pinned) | 5e123abc2480f0c4b4cac186d3b3f09299c258fc |
| Fetched at | 2026-09-04T05:30:43Z |
| License at fetch | mit |
| Snapshot tool | huggingface · seedbank 0.1.0 |
Trackers
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✓ verified · rehash-vs-hf-metadata at 2026-09-04T05:30:45Z
mit43.2 MB (45,341,280 bytes)transformerspytorchBERTMNLINLItransformerpre-trainingendpoints_compatible1 language (en)paper: 1908.08962paper: 2110.01518