Summarization
Transformers
PyTorch
Safetensors
bart
text2text-generation
Generated from Trainer
Eval Results (legacy)
Instructions to use morenolq/bart-base-xsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use morenolq/bart-base-xsum 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="morenolq/bart-base-xsum")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("morenolq/bart-base-xsum") model = AutoModelForSeq2SeqLM.from_pretrained("morenolq/bart-base-xsum") - Notebooks
- Google Colab
- Kaggle
Moreno La Quatra commited on
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README.md
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# bart-base-xsum
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This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on [xsum](https://huggingface.co/datasets/xsum) dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.8051
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# bart-base-xsum
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**Training:** The model has been trained using the script provided in the following repository https://github.com/MorenoLaQuatra/transformers-tasks-templates
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This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on [xsum](https://huggingface.co/datasets/xsum) dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.8051
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