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---
language:
- tr
- en
tags:
- mt5
- t5
- text-generation-inference
- turkish
widget:
- text: >-
Bu hafta hasta olduğum için <extra_id_0> gittim. Midem ağrıyordu ondan
dolayı şu an <extra_id_1>.
- example_title: Turkish Example 1
- text: Bu gece kar yağacakmış. Yarın yollarda <extra_id_0> olabilir.
- example_title: Turkish Example 2
- text: I bought two tickets for NBA match. Do you like <extra_id_0> ?
- example_title: English Example 2
---
# Model Card
<!-- Provide a quick summary of what the model is/does. -->
Please check [**google/mt5-base**](https://huggingface.co/google/mt5-base) model. This model is pruned version of mt5-base model to only work in Turkish and English. Also for methodology, you can check Russian version of mT5-base [cointegrated/rut5-base](https://huggingface.co/cointegrated/rut5-base).
# Usage
You should import required libraries by:
```python
from transformers import T5ForConditionalGeneration, T5Tokenizer
import torch
```
To load model:
```python
model = T5ForConditionalGeneration.from_pretrained('bonur/t5-base-tr')
tokenizer = T5Tokenizer.from_pretrained('bonur/t5-base-tr')
```
To make inference with given text, you can use the following code:
```python
inputs = tokenizer("Bu hafta hasta olduğum için <extra_id_0> gittim.", return_tensors='pt')
with torch.no_grad():
hypotheses = model.generate(
**inputs,
do_sample=True, top_p=0.95,
num_return_sequences=2,
repetition_penalty=2.75,
max_length=32,
)
for h in hypotheses:
print(tokenizer1.decode(h))
```
You can tune parameters for better result, and this model is ready to fine-tune in bilingual downstream tasks with English and Turkish.
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