Transformers
PyTorch
Safetensors
t5
text2text-generation
Trinidadian Creole
Caribbean dialect
text-generation-inference
Instructions to use KES/ENG-TEC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KES/ENG-TEC with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("KES/ENG-TEC") model = AutoModelForSeq2SeqLM.from_pretrained("KES/ENG-TEC") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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tokenizer = AutoTokenizer.from_pretrained("KES/ENG-TEC")
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model = AutoModelForSeq2SeqLM.from_pretrained("KES/ENG-TEC")
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text = "Where you going now?"
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inputs = tokenizer("eng:"+text, truncation=True, return_tensors='pt')
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output = model.generate(inputs['input_ids'], num_beams=4, max_length=512, early_stopping=True)
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translation=tokenizer.batch_decode(output, skip_special_tokens=True)
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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tokenizer = AutoTokenizer.from_pretrained("KES/ENG-TEC")
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model = AutoModelForSeq2SeqLM.from_pretrained("KES/ENG-TEC")
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text = "Where are you going now?"
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inputs = tokenizer("eng:"+text, truncation=True, return_tensors='pt')
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output = model.generate(inputs['input_ids'], num_beams=4, max_length=512, early_stopping=True)
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translation=tokenizer.batch_decode(output, skip_special_tokens=True)
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