readme: add initial version
Browse files
README.md
ADDED
|
@@ -0,0 +1,78 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
language: tr
|
| 3 |
+
license: mit
|
| 4 |
+
datasets:
|
| 5 |
+
- allenai/c4
|
| 6 |
+
---
|
| 7 |
+
|
| 8 |
+
# 🇹🇷 Turkish ELECTRA model
|
| 9 |
+
|
| 10 |
+
<p align="center">
|
| 11 |
+
<img alt="Logo provided by Merve Noyan" title="Awesome logo from Merve Noyan" src="https://raw.githubusercontent.com/stefan-it/turkish-bert/master/merve_logo.png">
|
| 12 |
+
</p>
|
| 13 |
+
|
| 14 |
+
[](https://zenodo.org/badge/latestdoi/237817454)
|
| 15 |
+
|
| 16 |
+
We present community-driven BERT, DistilBERT, ELECTRA and ConvBERT models for Turkish 🎉
|
| 17 |
+
|
| 18 |
+
Some datasets used for pretraining and evaluation are contributed from the
|
| 19 |
+
awesome Turkish NLP community, as well as the decision for the BERT model name: BERTurk.
|
| 20 |
+
|
| 21 |
+
Logo is provided by [Merve Noyan](https://twitter.com/mervenoyann).
|
| 22 |
+
|
| 23 |
+
# Stats
|
| 24 |
+
|
| 25 |
+
We've also trained an ELECTRA (uncased) model on the recently released Turkish part of the
|
| 26 |
+
[multiligual C4 (mC4) corpus](https://github.com/allenai/allennlp/discussions/5265) from the AI2 team.
|
| 27 |
+
|
| 28 |
+
After filtering documents with a broken encoding, the training corpus has a size of 242GB resulting
|
| 29 |
+
in 31,240,963,926 tokens.
|
| 30 |
+
|
| 31 |
+
We used the original 32k vocab (instead of creating a new one).
|
| 32 |
+
|
| 33 |
+
# mC4 ELECTRA
|
| 34 |
+
|
| 35 |
+
In addition to the ELEC**TR**A base cased model, we also trained an ELECTRA uncased model on the Turkish part of the mC4 corpus. We use a
|
| 36 |
+
sequence length of 512 over the full training time and train the model for 1M steps on a v3-32 TPU.
|
| 37 |
+
|
| 38 |
+
# Model usage
|
| 39 |
+
|
| 40 |
+
All trained models can be used from the [DBMDZ](https://github.com/dbmdz) Hugging Face [model hub page](https://huggingface.co/dbmdz)
|
| 41 |
+
using their model name.
|
| 42 |
+
|
| 43 |
+
Example usage with 🤗/Transformers:
|
| 44 |
+
|
| 45 |
+
```python
|
| 46 |
+
tokenizer = AutoTokenizer.from_pretrained("electra-base-turkish-mc4-uncased-generator")
|
| 47 |
+
|
| 48 |
+
model = AutoModel.from_pretrained("electra-base-turkish-mc4-uncased-generator")
|
| 49 |
+
```
|
| 50 |
+
|
| 51 |
+
# Citation
|
| 52 |
+
|
| 53 |
+
You can use the following BibTeX entry for citation:
|
| 54 |
+
|
| 55 |
+
```bibtex
|
| 56 |
+
@software{stefan_schweter_2020_3770924,
|
| 57 |
+
author = {Stefan Schweter},
|
| 58 |
+
title = {BERTurk - BERT models for Turkish},
|
| 59 |
+
month = apr,
|
| 60 |
+
year = 2020,
|
| 61 |
+
publisher = {Zenodo},
|
| 62 |
+
version = {1.0.0},
|
| 63 |
+
doi = {10.5281/zenodo.3770924},
|
| 64 |
+
url = {https://doi.org/10.5281/zenodo.3770924}
|
| 65 |
+
}
|
| 66 |
+
```
|
| 67 |
+
|
| 68 |
+
# Acknowledgments
|
| 69 |
+
|
| 70 |
+
Thanks to [Kemal Oflazer](http://www.andrew.cmu.edu/user/ko/) for providing us
|
| 71 |
+
additional large corpora for Turkish. Many thanks to Reyyan Yeniterzi for providing
|
| 72 |
+
us the Turkish NER dataset for evaluation.
|
| 73 |
+
|
| 74 |
+
We would like to thank [Merve Noyan](https://twitter.com/mervenoyann) for the
|
| 75 |
+
awesome logo!
|
| 76 |
+
|
| 77 |
+
Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC).
|
| 78 |
+
Thanks for providing access to the TFRC ❤️
|