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---
language: et
---
# EstBERT


### What's this?
The EstBERT model is a pretrained BERT<sub>Base</sub> model exclusively trained on Estonian cased corpus on both 128 and 512 sequence length of data.

### How to use?
You can use the model transformer library both in tensorflow and pytorch version.
```
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("tartuNLP/EstBERT")
model = AutoModelForMaskedLM.from_pretrained("tartuNLP/EstBERT")
```
You can also download the pretrained model from here, [EstBERT_128]() [EstBERT_512]()
#### Dataset used to train the model
The EstBERT model is trained both on 128 and 512 sequence length of data. For training the EstBERT we used the [Estonian National Corpus 2017](https://metashare.ut.ee/repository/browse/estonian-national-corpus-2017/b616ceda30ce11e8a6e4005056b40024880158b577154c01bd3d3fcfc9b762b3/), which was the largest Estonian language corpus available at the time. It consists of four sub-corpora: Estonian Reference Corpus 1990-2008, Estonian Web Corpus 2013, Estonian Web Corpus 2017 and Estonian Wikipedia Corpus 2017.
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If possible you should add dataset ids to your metadata. Even if those are not implemented in huggingface/datasets yet, this will prompt us (or the community) to add them at some point.

cc @lhoestq @thomwolf


### Why would I use?
Overall EstBERT performs better in parts of speech (POS), name entity recognition (NER), rubric, and sentiment classification tasks compared to mBERT and XLM-RoBERTa. The comparative results can be found below;

|Model |UPOS |XPOS |Morph |bf UPOS |bf XPOS |Morph |
|--------------|----------------------------|-------------|-------------|-------------|----------------------------|----------------------------|
| EstBERT | **_97.89_** | **98.40** | **96.93** | **97.84** | **_98.43_** | **_96.80_** |
| mBERT | 97.42 | 98.06 | 96.24 | 97.43 | 98.13 | 96.13 |
| XLM-RoBERTa | 97.78 | 98.36 | 96.53 | 97.80 | 98.40 | 96.69 |


|Model|Rubric<sub>128</sub> |Sentiment<sub>128</sub> | Rubric<sub>128</sub> |Sentiment<sub>512</sub> |
|-------------------|----------------------------|--------------------|-----------------------------------------------|----------------------------|
| EstBERT | **_81.70_** | 74.36 | **80.96** | 74.50 |
| mBERT | 75.67 | 70.23 | 74.94 | 69.52 |
| XLM\-RoBERTa | 80.34 | **74.50** | 78.62 | **_76.07_**|

|Model |Precicion<sub>128</sub> |Recall<sub>128</sub> |F1-Score<sub>128</sub> |Precision<sub>512</sub> |Recall<sub>512</sub> |F1-Score<sub>512</sub> |
|--------------|----------------|----------------------------|----------------------------|----------------------------|-------------|----------------|
| EstBERT | **88.42** | 90.38 |**_89.39_** | 88.35 | 89.74 | 89.04 |
| mBERT | 85.88 | 87.09 | 86.51 |**_88.47_** | 88.28 | 88.37 |
| XLM\-RoBERTa | 87.55 |**_91.19_** | 89.34 | 87.50 | **90.76** | **89.10** |