Translation
Transformers
Safetensors
English
Telugu
m2m_100
text2text-generation
text-generation
fine-tuned-model
colloquial-language
telugu
machine-translation
Instructions to use anithasoma/nllb-finetuned-telugu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anithasoma/nllb-finetuned-telugu 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="anithasoma/nllb-finetuned-telugu")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("anithasoma/nllb-finetuned-telugu") model = AutoModelForSeq2SeqLM.from_pretrained("anithasoma/nllb-finetuned-telugu") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bd93be8eebf93b9cc1947c5f17a53dafb03ccd122f738b75f4b9a7597991f692
- Size of remote file:
- 14.2 kB
- SHA256:
- 693743959ade24c03dddc29c66639d9bcbf7b2bbb7ad9166df4c8f057116d4da
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.