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  ---
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  license: cc-by-nc-4.0
 
 
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  ---
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  # Model Card for Spivavtor-Large
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- This model was obtained by fine-tuning the corresponding `bigscience/mt0-large` model on the Spivavtor dataset. All details of the dataset and fine tuning process can be found in our paper and repository.
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  **Paper:** Spivavtor: An Instruction Tuned Ukrainian Text Editing Model
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  - **Finetuned from model:** bigscience/mt0-large
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  ## How to use
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- We make available the following models presented in our paper.
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  <table>
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  <tr>
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  <tr>
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  <td>Spivavtor-large</td>
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  <td>1.2B</td>
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- <td>Spivavtor-mt0-large</td>
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  </tr>
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  <tr>
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  <td>Spivavtor-xxl</td>
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  <td>11B</td>
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- <td>Spivavtor-aya-101</td>
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  </tr>
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  </table>
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@@ -45,8 +47,9 @@ tokenizer = AutoTokenizer.from_pretrained("grammarly/spivavtor-large")
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  model = AutoModelForSeq2SeqLM.from_pretrained("grammarly/spivavtor-large")
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  input_text = 'Виправте граматику в цьому реченнi: Дякую за iнформацiю! ми з Надiєю саме вийшли з дому'
 
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- input_ids = tokenizer(input_text, return_tensors="pt").input_ids
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- outputs = model.generate(input_ids, max_length=256)
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  output_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
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  ```
 
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  ---
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  license: cc-by-nc-4.0
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+ language:
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+ - uk
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  ---
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  # Model Card for Spivavtor-Large
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+ This model was obtained by fine-tuning the corresponding `bigscience/mt0-large` model on the Spivavtor dataset. All details of the dataset and fine tuning process can be found in our paper.
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  **Paper:** Spivavtor: An Instruction Tuned Ukrainian Text Editing Model
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  - **Finetuned from model:** bigscience/mt0-large
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  ## How to use
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+ We make the following models available from our paper.
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  <table>
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  <tr>
 
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  <tr>
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  <td>Spivavtor-large</td>
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  <td>1.2B</td>
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+ <td>SPIVAVTOR-MT0-LARGE</td>
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  </tr>
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  <tr>
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  <td>Spivavtor-xxl</td>
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  <td>11B</td>
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+ <td>SPIVAVTOR-AYA-101</td>
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  </tr>
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  </table>
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  model = AutoModelForSeq2SeqLM.from_pretrained("grammarly/spivavtor-large")
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  input_text = 'Виправте граматику в цьому реченнi: Дякую за iнформацiю! ми з Надiєю саме вийшли з дому'
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+ # English translation of text: "Paraphrase the sentence: What is the greatest compliment that you ever received from anyone?"
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+ inputs = tokenizer.encode(input_text, return_tensors="pt")
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+ output = model.generate(inputs, max_length=256)
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  output_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
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  ```