Update README.md

README.md

@@ -1,115 +0,0 @@
----
-language:
-- en
-license: cc-by-nc-4.0
-datasets:
-- facebook/asset
-- wi_locness
-- GEM/wiki_auto_asset_turk
-- discofuse
-- zaemyung/IteraTeR_plus
-- jfleg
-- grammarly/coedit
-metrics:
-- sari
-- bleu
-- accuracy
-widget:
-- text: 'Fix the grammar: When I grow up, I start to understand what he said is quite
-    right.'
-  example_title: Fluency
-- text: 'Make this text coherent: Their flight is weak. They run quickly through the
-    tree canopy.'
-  example_title: Coherence
-- text: 'Rewrite to make this easier to understand: A storm surge is what forecasters
-    consider a hurricane''s most treacherous aspect.'
-  example_title: Simplification
-- text: 'Paraphrase this: Do you know where I was born?'
-  example_title: Paraphrase
-- text: 'Write this more formally: omg i love that song im listening to it right now'
-  example_title: Formalize
-- text: 'Write in a more neutral way: The authors'' exposé on nutrition studies.'
-  example_title: Neutralize
----
-# Model Card for CoEdIT-Large
-
-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.
-
-**Paper:** CoEdIT: Text Editing by Task-Specific Instruction Tuning
-
-**Authors:** Vipul Raheja, Dhruv Kumar, Ryan Koo, Dongyeop Kang
-
-## Model Details
-
-### Model Description
-
-- **Language(s) (NLP)**: English
-- **Finetuned from model:** google/flan-t5-large
-
-### Model Sources
-
-- **Repository:** https://github.com/vipulraheja/coedit
-- **Paper:** https://arxiv.org/abs/2305.09857
-
-## How to use
-We make available the models presented in our paper. 
-
-<table>
-  <tr>
-    <th>Model</th>
-    <th>Number of parameters</th>
-  </tr>
-  <tr>
-    <td>CoEdIT-large</td>
-    <td>770M</td>
-  </tr>
-  <tr>
-    <td>CoEdIT-xl</td>
-    <td>3B</td>
-  </tr>
-  <tr>
-    <td>CoEdIT-xxl</td>
-    <td>11B</td>
-  </tr>  
-</table>
-
-
-## Uses
-
-## Text Revision Task
-Given an edit instruction and an original text, our model can generate the edited version of the text.<br>
-
-![task_specs](https://huggingface.co/grammarly/coedit-xl/resolve/main/task_examples.png)
-
-## Usage
-```python
-from transformers import AutoTokenizer, T5ForConditionalGeneration
-
-tokenizer = AutoTokenizer.from_pretrained("grammarly/coedit-large")
-model = T5ForConditionalGeneration.from_pretrained("grammarly/coedit-large")
-input_text = 'Fix grammatical errors in this sentence: When I grow up, I start to understand what he said is quite right.'
-input_ids = tokenizer(input_text, return_tensors="pt").input_ids
-outputs = model.generate(input_ids, max_length=256)
-edited_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
-```
-
-
-#### Software
-https://github.com/vipulraheja/coedit
-
-## Citation
-
-**BibTeX:**
-```
-@article{raheja2023coedit,
-      title={CoEdIT: Text Editing by Task-Specific Instruction Tuning}, 
-      author={Vipul Raheja and Dhruv Kumar and Ryan Koo and Dongyeop Kang},
-      year={2023},
-      eprint={2305.09857},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL}
-}
-```
-
-**APA:**
-Raheja, V., Kumar, D., Koo, R., & Kang, D. (2023). CoEdIT: Text Editing by Task-Specific Instruction Tuning. ArXiv. /abs/2305.09857