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--- |
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language: |
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- tr |
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- en |
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tags: |
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- mt5 |
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- t5 |
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- text-generation-inference |
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- turkish |
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widget: |
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- text: >- |
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Bu hafta hasta olduğum için <extra_id_0> gittim. Midem ağrıyordu ondan |
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dolayı şu an <extra_id_1>. |
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- example_title: Turkish Example 1 |
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- text: Bu gece kar yağacakmış. Yarın yollarda <extra_id_0> olabilir. |
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- example_title: Turkish Example 2 |
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- text: I bought two tickets for NBA match. Do you like <extra_id_0> ? |
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- example_title: English Example 2 |
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--- |
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# Model Card |
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<!-- Provide a quick summary of what the model is/does. --> |
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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). |
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# Usage |
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You should import required libraries by: |
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```python |
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from transformers import T5ForConditionalGeneration, T5Tokenizer |
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import torch |
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``` |
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To load model: |
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```python |
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model = T5ForConditionalGeneration.from_pretrained('bonur/t5-base-tr') |
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tokenizer = T5Tokenizer.from_pretrained('bonur/t5-base-tr') |
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``` |
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To make inference with given text, you can use the following code: |
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```python |
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inputs = tokenizer("Bu hafta hasta olduğum için <extra_id_0> gittim.", return_tensors='pt') |
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with torch.no_grad(): |
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hypotheses = model.generate( |
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**inputs, |
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do_sample=True, top_p=0.95, |
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num_return_sequences=2, |
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repetition_penalty=2.75, |
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max_length=32, |
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) |
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for h in hypotheses: |
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print(tokenizer1.decode(h)) |
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``` |
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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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