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ENG-TEC
KES/ENG-TEC
ENG-TEC is a machine learning model from KES. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
This model utilises T5-base pre-trained model. It was fine tuned using a custom dataset for translation of English to Trinidad English Creole. This model will be updated periodically as more data is compiled. For more…
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At a glance
- Library
- transformers
- License
- apache-2.0
- Model type
- t5
- Access
- Public
- Created
- Oct 2, 2022
- Updated
- Jul 15, 2025
- SHA
- 880f66ba
Model card
From the Hugging Face model README
Standard English to Trinidad English Creole Translator
This model utilises T5-base pre-trained model. It was fine tuned using a custom dataset for translation of English to Trinidad English Creole. This model will be updated periodically as more data is compiled. For more on the Caribbean English Creole checkout the library Caribe.
Usage with Transformers
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("KES/ENG-TEC")
model = AutoModelForSeq2SeqLM.from_pretrained("KES/ENG-TEC")
text = "Where are you going now?"
inputs = tokenizer("eng:"+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: Weh yuh going now.
- Library
- transformers
- Type
- t5
- License
- apache-2.0
- Created
- Oct 2, 2022
- Updated
- Jul 15, 2025