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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/Aelzi/25fee0b38c4687a2e9821d87980bbb09/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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This model is a fine-tuned version of [pszemraj/flan-t5-large-grammar-synthesis](https://huggingface.co/pszemraj/flan-t5-large-grammar-synthesis) using the C4 200M dataset for the NaraSpeak Bangkit 2024 ENTR-H130 application. |
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## T5 Model Overview |
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The T5 (Text-To-Text Transfer Transformer) model, introduced by Google Research, is a transformer-based model that treats every NLP task as a text-to-text problem. This unified approach allows T5 to excel at a variety of tasks, such as translation, summarization, and question answering, by converting inputs and outputs into text format. |
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### Transformer Architecture |
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Transformers are a type of deep learning model designed for sequence-to-sequence tasks. They utilize a mechanism called "attention" to weigh the influence of different words in a sequence, allowing the model to focus on relevant parts of the input when generating each word in the output. This architecture is highly parallelizable and has proven effective in NLP tasks. |
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## Usage in Python |
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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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