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--- |
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languages: |
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- en |
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license: |
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- cc-by-nc-sa-4.0 |
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- apache-2.0 |
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tags: |
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- grammar |
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- spelling |
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- punctuation |
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- error-correction |
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- grammar synthesis |
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- FLAN |
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- C4 |
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datasets: |
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- C4 |
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widget: |
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- text: "Me go to the store yesterday and buy many thing. I saw a big dog but he no bark at me. Then I walk home and eat my lunch, it was delicious sandwich. After that, I watch TV and see a funny show about cat who can talk. I laugh so hard I cry. Then I go to bed but I no can sleep because I too excited about the cat show." |
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example_title: "Long-Text" |
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- text: "Me and my family go on a trip to the mountains last week. We drive for many hours and finally reach our cabin. The cabin was cozy and warm, with a fireplace and big windows. We spend our days hiking and exploring the forest. At night, we sit by the fire and tell story. It was a wonderful vacation." |
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example_title: "Long-Text" |
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- text: "so em if we have an now so with fito ringina know how to estimate the tren given the ereafte mylite trend we can also em an estimate is nod s i again tort watfettering an we have estimated the trend an called wot to be called sthat of exty right now we can and look at wy this should not hare a trend i becan we just remove the trend an and we can we now estimate tesees ona effect of them exty" |
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example_title: "Transcribed Audio Example" |
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- text: "My coworker said he used a financial planner to help choose his stocks so he wouldn't loose money." |
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example_title: "incorrect word choice" |
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- text: "good so hve on an tadley i'm not able to make it to the exla session on monday this week e which is why i am e recording pre recording an this excelleision and so to day i want e to talk about two things and first of all em i wont em wene give a summary er about ta ohow to remove trents in these nalitives from time series" |
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example_title: "lowercased audio transcription output" |
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parameters: |
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max_length: 128 |
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min_length: 4 |
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num_beams: 8 |
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repetition_penalty: 1.21 |
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length_penalty: 1 |
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early_stopping: True |
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--- |
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# Grammar-Synthesis-Enhanced: FLAN-t5 |
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<a href="https://colab.research.google.com/gist/pszemraj/5dc89199a631a9c6cfd7e386011452a0/demo-flan-t5-large-grammar-synthesis.ipynb"> |
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<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> |
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</a> |
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A fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) for grammar correction on an expanded version of the [JFLEG](https://paperswithcode.com/dataset/jfleg) dataset and further fine-tuned using the [C4 200M](https://www.tensorflow.org/datasets/community_catalog/huggingface/c4) dataset. [Demo](https://huggingface.co/spaces/pszemraj/FLAN-grammar-correction) on HF spaces. |
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## Example |
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Compare vs. the original [grammar-synthesis-large](https://huggingface.co/pszemraj/grammar-synthesis-large). |
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--- |
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## Usage in Python |
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> There's a colab notebook that already has this basic version implemented (_click on the Open in Colab button_) |
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After `pip install transformers` run the following code: |
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```python |
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from transformers import pipeline |
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corrector = pipeline( |
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'text2text-generation', |
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'farelzii/GEC_Test_v1', |
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) |
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raw_text = 'i can has cheezburger' |
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results = corrector(raw_text) |
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print(results) |
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