Summarization
Transformers
PyTorch
Safetensors
bart
text2text-generation
Generated from Trainer
hindi
seq2seq
Instructions to use Someman/bart-hindi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Someman/bart-hindi with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" 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("summarization", model="Someman/bart-hindi")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Someman/bart-hindi") model = AutoModelForSeq2SeqLM.from_pretrained("Someman/bart-hindi", device_map="auto") - Notebooks
- Google Colab
- Kaggle
added dataset
Browse files
README.md
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# bart-hindi
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This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the
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It achieves the following results on the evaluation set:
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- Loss: 0.4985
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# bart-hindi
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This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the [Someman/bart-summarization](https://huggingface.co/Someman/hindi-summarization) dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4985
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