Transformers
Safetensors
t5
text2text-generation
Guyanese Creole
Caribbean dialect
text-generation-inference
Instructions to use KES/GEC-English with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KES/GEC-English with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("KES/GEC-English") model = AutoModelForSeq2SeqLM.from_pretrained("KES/GEC-English") - Notebooks
- Google Colab
- Kaggle
Guyanese English Creole to English Translator
This model utilises T5-base pre-trained model. It was fine tuned using a custom dataset for translation of Guyanese English Creole to English. This model will be updated periodically as more data is compiled. For more on the Caribbean English Creoles checkout the library Caribe.
Usage with Transformers
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("KES/GEC-English")
model = AutoModelForSeq2SeqLM.from_pretrained("KES/GEC-English")
text = "Ah waan ah phone"
inputs = tokenizer("guy:"+text, truncation=True, return_tensors='pt')
output = model.generate(inputs['input_ids'], num_beams=4, max_length=512, early_stopping=True)
translation=tokenizer.batch_decode(output, skip_special_tokens=True)
print("".join(translation)) #translation: I want a phone.
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