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HARSHU550
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Grammer

Text2Text Generation
Transformers
PyTorch
English
t5
text-generation-inference
Inference Endpoints
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  1. .gitattributes +34 -0
  2. README.md +115 -0
  3. config.json +32 -0
  4. generation_config.json +7 -0
  5. pytorch_model.bin +3 -0
  6. special_tokens_map.json +107 -0
  7. spiece.model +3 -0
  8. tokenizer.json +0 -0
  9. tokenizer_config.json +112 -0
  10. trainer_state.json +3392 -0
  11. training_args.bin +3 -0
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README.md ADDED
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+ ---
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+ language:
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+ - en
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+ license: cc-by-nc-4.0
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+ datasets:
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+ - facebook/asset
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+ - wi_locness
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+ - GEM/wiki_auto_asset_turk
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+ - discofuse
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+ - zaemyung/IteraTeR_plus
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+ - jfleg
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+ - grammarly/coedit
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+ metrics:
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+ - sari
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+ - bleu
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+ - accuracy
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+ widget:
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+ - text: 'Fix the grammar: When I grow up, I start to understand what he said is quite
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+ right.'
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+ example_title: Fluency
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+ - text: 'Make this text coherent: Their flight is weak. They run quickly through the
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+ tree canopy.'
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+ example_title: Coherence
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+ - text: 'Rewrite to make this easier to understand: A storm surge is what forecasters
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+ consider a hurricane''s most treacherous aspect.'
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+ example_title: Simplification
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+ - text: 'Paraphrase this: Do you know where I was born?'
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+ example_title: Paraphrase
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+ - text: 'Write this more formally: omg i love that song im listening to it right now'
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+ example_title: Formalize
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+ - text: 'Write in a more neutral way: The authors'' exposé on nutrition studies.'
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+ example_title: Neutralize
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+ ---
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+ # Model Card for CoEdIT-Large
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+
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+ This model was obtained by fine-tuning the corresponding `google/flan-t5-large` model on the CoEdIT dataset. Details of the dataset can be found in our paper and repository.
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+
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+ **Paper:** CoEdIT: Text Editing by Task-Specific Instruction Tuning
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+
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+ **Authors:** Vipul Raheja, Dhruv Kumar, Ryan Koo, Dongyeop Kang
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+
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+ ## Model Details
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+
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+ ### Model Description
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+
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+ - **Language(s) (NLP)**: English
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+ - **Finetuned from model:** google/flan-t5-large
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+
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+ ### Model Sources
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+
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+ - **Repository:** https://github.com/vipulraheja/coedit
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+ - **Paper:** https://arxiv.org/abs/2305.09857
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+
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+ ## How to use
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+ We make available the models presented in our paper.
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+
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+ <table>
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+ <tr>
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+ <th>Model</th>
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+ <th>Number of parameters</th>
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+ </tr>
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+ <tr>
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+ <td>CoEdIT-large</td>
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+ <td>770M</td>
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+ </tr>
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+ <tr>
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+ <td>CoEdIT-xl</td>
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+ <td>3B</td>
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+ </tr>
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+ <tr>
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+ <td>CoEdIT-xxl</td>
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+ <td>11B</td>
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+ </tr>
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+ </table>
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+
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+
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+ ## Uses
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+
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+ ## Text Revision Task
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+ Given an edit instruction and an original text, our model can generate the edited version of the text.<br>
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+
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+ ![task_specs](https://huggingface.co/grammarly/coedit-xl/resolve/main/task_examples.png)
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+
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+ ## Usage
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+ ```python
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+ from transformers import AutoTokenizer, T5ForConditionalGeneration
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+
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+ tokenizer = AutoTokenizer.from_pretrained("grammarly/coedit-large")
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+ model = T5ForConditionalGeneration.from_pretrained("grammarly/coedit-large")
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+ input_text = 'Fix grammatical errors in this sentence: When I grow up, I start to understand what he said is quite right.'
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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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+ edited_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
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+ ```
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+
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+
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+ #### Software
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+ https://github.com/vipulraheja/coedit
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+
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+ ## Citation
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+
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+ **BibTeX:**
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+ ```
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+ @article{raheja2023coedit,
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+ title={CoEdIT: Text Editing by Task-Specific Instruction Tuning},
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+ author={Vipul Raheja and Dhruv Kumar and Ryan Koo and Dongyeop Kang},
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+ year={2023},
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+ eprint={2305.09857},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CL}
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+ }
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+ ```
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+
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+ **APA:**
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+ Raheja, V., Kumar, D., Koo, R., & Kang, D. (2023). CoEdIT: Text Editing by Task-Specific Instruction Tuning. ArXiv. /abs/2305.09857
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