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saharM/
bart-base-finetuned-samsum

Name: bart-base-finetuned-samsum
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BART Base Fine-Tuned on SAMSum for Dialogue Summarization

This model is a fine-tuned version of facebook/bart-base trained on the SAMSum dataset for the task of dialogue summarization.

Model Details

Architecture: BART-base (Sequence-to-Sequence Transformer)
Fine-tuned on: SAMSum dataset (dialogue conversations with human-written summaries)
Use case: Summarizing chat/dialogue conversations into concise summaries

Usage

python

from transformers import BartForConditionalGeneration, BartTokenizer

model_name = "your-username/bart-finetuned-samsum"  # replace with your model repo name

tokenizer = BartTokenizer.from_pretrained(model_name)
model = BartForConditionalGeneration.from_pretrained(model_name)

dialogue = """
Speaker 1: Hey, are you coming to the party tonight?
Speaker 2: I’m not sure yet, maybe. What time does it start?
Speaker 1: Around 8 PM. Let me know!
"""

inputs = tokenizer(dialogue, return_tensors="pt")
summary_ids = model.generate(**inputs)
summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)

print("Summary:", summary)

Dataset

The SAMSum dataset is a collection of messenger-like conversations with corresponding summaries, designed to train and evaluate dialogue summarization models.

Dataset link

License

This model and code are licensed under the MIT License.

Citation

If you use this model in your research, please cite:

bibtex

@inproceedings{gliwa2019samsum,
  title={SAMSum Corpus: A Human-Annotated Dialogue Dataset for Abstractive Summarization},
  author={Gliwa, Bogdan and Wójcik, Tomasz and Biega, Agnieszka and Marasek, Konrad},
  booktitle={Proceedings of the 2nd Workshop on New Frontiers in Summarization},
  year={2019}
}

saharM/bart-base-finetuned-samsum