We present community-driven BERT, DistilBERT, ELECTRA and ConvBERT models for Turkish 🎉
Some datasets used for pretraining and evaluation are contributed from the awesome Turkish NLP community, as well as the decision for the BERT model name: BERTurk.
Logo is provided by Merve Noyan.
- 21.12.2024: New evaluations with Flair are added.
- 23.09.2021: Release of uncased ELECTRA and ConvBERT models and cased ELECTRA model, all trained on mC4 corpus.
- 24.06.2021: Release of new ELECTRA model, trained on Turkish part of mC4 dataset. Repository got new awesome logo from Merve Noyan.
- 16.03.2021: Release of ConvBERTurk model and more evaluations on different downstream tasks.
- 12.05.2020: Release of ELECTRA (small and base) models, see here.
- 25.03.2020: Release of BERTurk uncased model and BERTurk models with larger vocab size (128k, cased and uncased).
- 11.03.2020: Release of the cased distilled BERTurk model: DistilBERTurk. Available on the Hugging Face model hub
- 17.02.2020: Release of the cased BERTurk model. Available on the Hugging Face model hub
- 10.02.2020: Training corpus update, new TensorBoard links, new results for cased model.
- 02.02.2020: Initial version of this repo.
The current version of the model is trained on a filtered and sentence segmented version of the Turkish OSCAR corpus, a recent Wikipedia dump, various OPUS corpora and a special corpus provided by Kemal Oflazer.
The final training corpus has a size of 35GB and 4,404,976,662 tokens.
Thanks to Google's TensorFlow Research Cloud (TFRC) we can train both cased and uncased models on a TPU v3-8. You can find the TensorBoard outputs for the training here:
We also provide cased and uncased models that aŕe using a larger vocab size (128k instead of 32k).
A detailed cheatsheet of how the models were trained, can be found here.
We've also trained an ELECTRA (cased) model on the recently released Turkish part of the multiligual C4 (mC4) corpus from the AI2 team.
After filtering documents with a broken encoding, the training corpus has a size of 242GB resulting in 31,240,963,926 tokens.
We used the original 32k vocab (instead of creating a new one).
Here's an overview of all available models, incl. their training corpus size:
| Model name | Model hub link | Pre-training corpus size |
|---|---|---|
| ELECTRA Small (cased) | here | 35GB |
| ELECTRA Base (cased) | here | 35GB |
| ELECTRA Base mC4 (cased) | here | 242GB |
| ELECTRA Base mC4 (uncased) | here | 242GB |
| BERTurk (cased, 32k) | here | 35GB |
| BERTurk (uncased, 32k) | here | 35GB |
| BERTurk (cased, 128k) | here | 35GB |
| BERTurk (uncased, 128k) | here | 35GB |
| DistilBERTurk (cased) | here | 35GB |
| ConvBERTurk (cased) | here | 35GB |
| ConvBERTurk mC4 (cased) | here | 242GB |
| ConvBERTurk mC4 (uncased) | here | 242GB |
The distilled version of a cased model, so called DistilBERTurk, was trained on 7GB of the original training data, using the cased version of BERTurk as teacher model.
DistilBERTurk was trained with the official Hugging Face implementation from here.
The cased model was trained for 5 days on 4 RTX 2080 TI.
More details about distillation can be found in the "DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter" paper by Sanh et al. (2019).
In addition to the BERTurk models, we also trained ELECTRA small and base models. A detailed overview can be found in the ELECTRA section.
In addition to the BERT and ELECTRA based models, we also trained a ConvBERT model. The ConvBERT architecture is presented in the "ConvBERT: Improving BERT with Span-based Dynamic Convolution" paper.
We follow a different training procedure: instead of using a two-phase approach, that pre-trains the model for 90% with 128 sequence length and 10% with 512 sequence length, we pre-train the model with 512 sequence length for 1M steps on a v3-32 TPU.
More details about the pre-training can be found here.
In addition to the ELECTRA base model, we also trained an ELECTRA model on the Turkish part of the mC4 corpus. We use a sequence length of 512 over the full training time and train the model for 1M steps on a v3-32 TPU.
In 2024 we ran new evaluations on PoS tagging, NER and sentiment classification datasets. Prior evaluation results can be found here.
All evaluations are performed with the awesome Flair library and the evaluation code and configs can be found in the `experiments folder of this repository.
The Model Zoo is evaluated on (the concatenation) of the following PoS Tagging datasets from Universal Dependencies:
We perform a hyper-parameter search over the following configurations:
| Parameter | Values |
|---|---|
| Batch Size | [16, 8] |
| Learning Rate | [3e-5, 5e-5] |
| Epoch | [3] |
And report averaged Accuracy over 5 runs (with different seeds):
| Model Name | Best Configuration | Best Development Score | Best Test Score |
|---|---|---|---|
| BERTurk (cased, 128k) | bs16-e3-lr5e-05 |
93.93 ± 0.04 | 94.50 ± 0.07 |
| BERTurk (uncased, 128k) | bs8-e3-lr5e-05 |
93.84 ± 0.04 | 94.41 ± 0.13 |
| BERTurk (cased, 32k) | bs16-e3-lr5e-05 |
93.95 ± 0.05 | 94.57 ± 0.04 |
| BERTurk (uncased, 32k) | bs16-e3-lr5e-05 |
93.84 ± 0.04 | 94.38 ± 0.03 |
| ConvBERTurk (cased) | bs8-e3-lr5e-05 |
94.03 ± 0.07 | 94.58 ± 0.06 |
| ConvBERTurk mC4 (cased) | coming soon! | ||
| ConvBERTurk mC4 (uncased) | bs8-e3-lr5e-05 |
93.90 ± 0.08 | 94.52 ± 0.04 |
| DistilBERTurk (cased) | bs8-e3-lr5e-05 |
93.52 ± 0.03 | 94.19 ± 0.04 |
| ELECTRA Base (cased) | bs16-e3-lr5e-05 |
93.89 ± 0.05 | 94.45 ± 0.05 |
| ELECTRA Base mC4 (cased) | bs16-e3-lr5e-05 |
93.88 ± 0.05 | 94.53 ± 0.11 |
| ELECTRA Base mC4 (uncased) | bs8-e3-lr5e-05 |
93.80 ± 0.09 | 94.41 ± 0.04 |
| ELECTRA Small (cased) | bs8-e3-lr5e-05 |
93.15 ± 0.04 | 93.88 ± 0.06 |
The Model Zoo is evaluated on the Turkish split of the WikiANN dataset, using the following hyper-parameter search:
| Parameter | Values |
|---|---|
| Batch Size | [16, 8] |
| Learning Rate | [3e-5, 5e-5] |
| Epoch | [10] |
Averaged F1-Score over 5 runs (with different seeds):
| Model Name | Best Configuration | Best Development Score | Best Test Score |
|---|---|---|---|
| BERTurk (cased, 128k) | bs8-e10-lr3e-05 |
93.92 ± 0.07 | 93.92 ± 0.16 |
| BERTurk (uncased, 128k) | bs16-e10-lr3e-05 |
93.59 ± 0.05 | 93.29 ± 0.11 |
| BERTurk (cased, 32k) | bs8-e10-lr3e-05 |
93.36 ± 0.04 | 93.26 ± 0.14 |
| BERTurk (uncased, 32k) | bs8-e10-lr3e-05 |
93.13 ± 0.19 | 92.96 ± 0.06 |
| ConvBERTurk (cased) | bs8-e10-lr3e-05 |
93.93 ± 0.07 | 93.93 ± 0.05 |
| ConvBERTurk mC4 (cased) | coming soon! | ||
| ConvBERTurk mC4 (uncased) | bs8-e10-lr3e-05 |
93.68 ± 0.13 | 93.58 ± 0.15 |
| DistilBERTurk (cased) | bs8-e10-lr5e-05 |
91.8 ± 0.05 | 91.17 ± 0.03 |
| ELECTRA Base (cased) | bs8-e10-lr3e-05 |
93.58 ± 0.12 | 93.60 ± 0.09 |
| ELECTRA Base mC4 (cased) | bs16-e10-lr3e-05 |
93.51 ± 0.09 | 93.42 ± 0.11 |
| ELECTRA Base mC4 (uncased) | bs16-e10-lr5e-05 |
93.01 ± 0.12 | 92.94 ± 0.13 |
| ELECTRA Small (cased) | bs8-e10-lr5e-05 |
91.42 ± 0.09 | 91.07 ± 0.09 |
The Model Zoo is additionally evaluated on the OffensEval-TR 2020 dataset for sentiment classification.
The following parameters are used for a hyper-parameter search:
| Parameter | Values |
|---|---|
| Batch Size | [16, 8] |
| Learning Rate | [3e-5, 5e-5] |
| Epoch | [3] |
Averaged Macro F1-Score over 5 runs (with different seeds) is reported:
| Model Name | Best Configuration | Best Development Score | Best Test Score |
|---|---|---|---|
| BERTurk (cased, 128k) | bs16-e3-lr3e-05 |
81.30 ± 0.61 | 81.72 ± 0.47 |
| BERTurk (uncased, 128k) | bs16-e3-lr3e-05 |
80.31 ± 0.54 | 82.16 ± 0.27 |
| BERTurk (cased, 32k) | bs16-e3-lr5e-05 |
79.64 ± 0.50 | 80.65 ± 0.40 |
| BERTurk (uncased, 32k) | bs16-e3-lr3e-05 |
80.87 ± 0.22 | 81.68 ± 0.37 |
| ConvBERTurk (cased) | bs16-e3-lr3e-05 |
82.22 ± 0.41 | 82.29 ± 0.34 |
| ConvBERTurk mC4 (cased) | coming soon! | ||
| ConvBERTurk mC4 (uncased) | bs16-e3-lr3e-05 |
81.69 ± 0.29 | 81.81 ± 0.37 |
| DistilBERTurk (cased) | bs16-e3-lr3e-05 |
78.54 ± 0.55 | 79.12 ± 0.17 |
| ELECTRA Base (cased) | bs16-e3-lr3e-05 |
79.76 ± 0.24 | 81.69 ± 0.38 |
| ELECTRA Base mC4 (cased) | bs8-e3-lr3e-05 |
80.34 ± 0.67 | 82.14 ± 0.27 |
| ELECTRA Base mC4 (uncased) | bs16-e3-lr5e-05 |
80.46 ± 0.80 | 81.52 ± 0.56 |
| ELECTRA Small (cased) | bs16-e3-lr5e-05 |
77.25 ± 0.47 | 79.89 ± 0.28 |
The following table shows the performance of all models over all datasets:
| Model Name | Overall Development | Overall Test |
|---|---|---|
| BERTurk (cased, 128k) | 89.72 | 90.05 |
| BERTurk (uncased, 128k) | 89.25 | 89.95 |
| BERTurk (cased, 32k) | 88.98 | 89.49 |
| BERTurk (uncased, 32k) | 89.28 | 89.67 |
| ConvBERTurk (cased) | 90.06 | 90.27 |
| ConvBERTurk mC4 (cased) | coming soon! | |
| ConvBERTurk mC4 (uncased) | 89.76 | 89.97 |
| DistilBERTurk (cased) | 87.95 | 88.16 |
| ELECTRA Base (cased) | 89.08 | 89.91 |
| ELECTRA Base mC4 (cased) | 89.24 | 90.03 |
| ELECTRA Base mC4 (uncased) | 89.09 | 89.62 |
| ELECTRA Small (cased) | 87.27 | 88.28 |
All trained models can be used from the DBMDZ Hugging Face model hub page using their model name.
Example usage with 🤗/Transformers:
tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-turkish-cased")
model = AutoModel.from_pretrained("dbmdz/bert-base-turkish-cased")This loads the BERTurk cased model. The recently introduced ELECTRA base model can be loaded with:
tokenizer = AutoTokenizer.from_pretrained("dbmdz/electra-base-turkish-cased-discriminator")
model = AutoModelWithLMHead.from_pretrained("dbmdz/electra-base-turkish-cased-discriminator")You can use the following BibTeX entry for citation:
@software{stefan_schweter_2020_3770924,
author = {Stefan Schweter},
title = {BERTurk - BERT models for Turkish},
month = apr,
year = 2020,
publisher = {Zenodo},
version = {1.0.0},
doi = {10.5281/zenodo.3770924},
url = {https://doi.org/10.5281/zenodo.3770924}
}Thanks to Kemal Oflazer for providing us additional large corpora for Turkish. Many thanks to Reyyan Yeniterzi for providing us the Turkish NER dataset for evaluation.
We would like to thank Merve Noyan for the awesome logo!
Research supported with Cloud TPUs from the awesome TRC program.
Many thanks for providing access to the TPUs ❤️
