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---
license: openrail
datasets:
- WelfCrozzo/kupalinka
language:
- be
- en
- ru
metrics:
- bleu
library_name: transformers
tags:
- translation
widget:
- text: "<extra_id_1>да зорак праз цяжкасці"
example_title: "be -> ru"
- text: "<extra_id_2>да зорак праз цяжкасці"
example_title: "be -> en"
- text: "<extra_id_3>к звездам через трудности"
example_title: "ru -> be"
- text: "<extra_id_5>к звездам через трудности"
example_title: "ru -> en"
- text: "<extra_id_6>to the stars through difficulties."
example_title: "en -> be"
- text: "<extra_id_7>to the stars through difficulties."
example_title: "en -> ru"
---
# T5 for belarusian language
This model is based on T5-small with sequence length equal 128 tokens. Model trained from scratch on RTX 3090 24GB.
# Supported tasks:
- translation BE to RU: `<extra_id_1>`
- translation BE to EN: `<extra_id_2>`
- translation RU to BE: `<extra_id_3>`
- translation RU to EN: `<extra_id_5>`
- translation EN to BE: `<extra_id_6>`
- translation EN to RU: `<extra_id_7>`
# Metrics:
- [evel/BLEU](https://api.wandb.ai/links/miklgr500/31mq4s36)
- [evel/loss](https://api.wandb.ai/links/miklgr500/rvi2p69n)
- [train/loss](https://api.wandb.ai/links/miklgr500/z9alu3n5)
# How to Get Started with the Model
<details>
<summary> Click to expand </summary>
```python
from transformers import T5TokenizerFast, T5ForConditionalGeneration
tokenizer = T5TokenizerFast.from_pretrained("WelfCrozzo/T5-L128-belarusian")
model = T5ForConditionalGeneration.from_pretrained("WelfCrozzo/T5-L128-belarusian")
x = tokenizer.encode('<extra_id_1>да зорак праз цяжкасці', return_tensors='pt')
result = model.generate(x, return_dict_in_generate=True, output_scores=True,max_length=128)
print(tokenizer.decode(result["sequences"][0]))
```
</details>
# References
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://jmlr.org/papers/volume21/20-074/20-074.pdf) |