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-mini bert-small and bert-medium.
Model Card
By Prajjwal, published under mit, revision 6f75de8b60a9.
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-mini 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: - prajjwal1/bert-tiny (L=2, H=128) Model Link - prajjwal1/bert-mini (L=4, H=256) Model Link…
Read Prajjwal's full model card
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-mini 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-tiny (L=2, H=128) Model Link
Other models to check out:
- prajjwal1/bert-mini (L=4, H=256) Model Link
- prajjwal1/bert-small (L=4, H=512) Model Link
- prajjwal1/bert-medium (L=8, H=512) Model Link
Original Implementation and more info can be found in this Github repository.
Twitter: @prajjwal_1
Configuration
- Context length (tokens)
- 512
- Layers
- 2
- Hidden size
- 128
- Feed-forward size
- 512
- Attention heads
- 2
- Vocabulary size
- 30,522
Identity and Version
- Repository
- prajjwal1/bert-tiny
- Publisher
- Prajjwal
- Task
- Not stated by the source
- Modality
- Other
- Library
- transformers
- Parameters
- Not stated by the source
- Languages
- en
- Revision
- 6f75de8b60a9f8a2fdf7b69cbd86d9e64bcb3837
- First published
- 2022-03-02
- Last updated
- 2021-10-27
Files and Weights
5 files, 18.0 MB in total. The weights are 1 file totalling 17.8 MB in bin.
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| pytorch_model.bin | Weights | 17.8 MB | dab2c2bddcfb |
| config.json | Configuration | 285 B | — |
| README.md | Documentation | 2.6 KB | — |
| .gitattributes | Repository | 345 B | — |
| vocab.txt | Tokenizer | 231.5 KB | — |
License and Download
- License
- mit
- Access
- Open weights, no gate
- Download size
- 17.8 MB
Released by Prajjwal through its official repository on Hugging Face. Read the license.
Built From
- Described by arXiv:1908.08962
- Described by arXiv:2110.01518
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 17.8 MB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About bert-tiny
Can I use bert-tiny commercially?
Yes. bert-tiny is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.
What is bert-tiny's context length?
512 tokens, from the maximum position embeddings in its published configuration.