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
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README.md
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license: apache-2.0
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datasets:
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- anithasoma/
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language:
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- en
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- te
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Click the badge above to launch the model in Google Colab!
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## Training Details
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- **Dataset:** anithasoma/
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- **Training Environment:** Google Colab with NVIDIA GPU.
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- **Fine-Tuning Method:** LoRA + PEFT (Parameter Efficient Fine-Tuning)
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- **Epochs:** Adjusted based on validation loss.
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license: apache-2.0
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datasets:
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- anithasoma/refined_en_te
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language:
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- en
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- te
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Click the badge above to launch the model in Google Colab!
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## Training Details
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- **Dataset:** anithasoma/refined_en_te
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- **Training Environment:** Google Colab with NVIDIA GPU.
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- **Fine-Tuning Method:** LoRA + PEFT (Parameter Efficient Fine-Tuning)
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- **Epochs:** Adjusted based on validation loss.
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