Transformers
PyTorch
Safetensors
English
t5
text2text-generation
sentence correction
text-generation-inference
Instructions to use KES/T5-KES with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KES/T5-KES with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("KES/T5-KES") model = AutoModelForSeq2SeqLM.from_pretrained("KES/T5-KES") - Notebooks
- Google Colab
- Kaggle
Merge branch 'main' of https://huggingface.co/KES/T5-KES into main
Browse files
README.md
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# Model
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This model utilises T5-base
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print(correction.text) # Correction: "What is your name?".
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```
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# Usage with Transformers
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```python
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print("".join(correction)) #Correction: I am living with my parents.
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```
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# Model
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This model utilises T5-base pre-trained model. It was fine tuned using a modified version of the [JFLEG](https://arxiv.org/abs/1702.04066) dataset and [Happy Transformer framework](https://github.com/EricFillion/happy-transformer). This model was fine-tuned for sentence correction on normal English translations and positional English translations of local Caribbean English Creole. This model will be updated periodically as more data is compiled. For more on the Caribbean English Creole checkout the library [Caribe](https://pypi.org/project/Caribe/).
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print(correction.text) # Correction: "What is your name?".
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```
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# Usage with Transformers
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```python
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print("".join(correction)) #Correction: I am living with my parents.
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```
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