# 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.

{"language": ["tr", "en"], "tags": ["mt5", "t5", "text-generation-inference", "turkish"], "widget": [{"text": "Bu hafta hasta oldu\u011fum i\u00e7in <extra_id_0> gittim. Midem a\u011fr\u0131yordu ondan dolay\u0131 \u015fu an <extra_id_1>."}, {"example_title": "Turkish Example 1"}, {"text": "Bu gece kar ya\u011facakm\u0131\u015f. Yar\u0131n 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"}]}
transformers pytorch safetensors t5 text2text-generation mt5 text-generation-inference turkish tr en autotrain_compatible endpoints_compatible region:us