Instructions to use Deepakvictor/tan-ta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Deepakvictor/tan-ta with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="Deepakvictor/tan-ta")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Deepakvictor/tan-ta") model = AutoModelForSeq2SeqLM.from_pretrained("Deepakvictor/tan-ta") - Notebooks
- Google Colab
- Kaggle
Model Card for Deepakvictor/tan-ta
This model is Finetuned on Facebook's m2m model to convert Tanglish words to Tamil
Model Details
Model is finetuned on facebook/m2m100_418M m2m100_418m page --> https://huggingface.co/facebook/m2m100_418M
Model Description
- Developed by: Deepakvictor
- Language(s) (NLP): Tamil,Tanglish
- Finetuned from model [facebook/m2m100_418M]: [https://huggingface.co/facebook/m2m100_418M]
Model Sources
- Repository: [Need to be uploaded]
- Demo [optional]: [Need to be uploaded]
How to Get Started with the Model
Use the code below to get started with the model.
# Load model directly from transformers library
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("Deepakvictor/tan-ta")
model = AutoModelForSeq2SeqLM.from_pretrained("Deepakvictor/tan-ta")
#pass the input
inp = tokenizer("Thalaivaru nirantharam",return_tensors="pt")
out= model.generate(**inp)
tokenizer.batch_decode(out,skip_special_tokens=True)
#['தலைவரு நிரந்தரம்']
Repo code --> github.com/devic1 🖤
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