Model Card

Please check 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.

Usage

You should import required libraries by:

from transformers import T5ForConditionalGeneration, T5Tokenizer
import torch

To load model:

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:

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.