Summarization
Transformers
Safetensors
English
bart
text2text-generation
Generated from Trainer
Eval Results (legacy)
Instructions to use Remeris/BART-CNN-Convosumm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Remeris/BART-CNN-Convosumm 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="Remeris/BART-CNN-Convosumm")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Remeris/BART-CNN-Convosumm") model = AutoModelForSeq2SeqLM.from_pretrained("Remeris/BART-CNN-Convosumm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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# BART-CNN-Convosumm
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This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the arg-filtered reddit part of Convosumm dataset.
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It achieves the following results on the evaluation set:
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- Loss: 3.8797
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- Rouge1: 38.6252
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# BART-CNN-Convosumm
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This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the arg-filtered reddit part of [Convosumm](https://github.com/Yale-LILY/ConvoSumm) dataset.
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It achieves the following results on the evaluation set:
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- Loss: 3.8797
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- Rouge1: 38.6252
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