README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
  - seq2seq
  - summarization
datasets:
  - samsum
metrics:
  - rouge
widget:
  - text: >
      Emily: Hey Alex, have you heard about the new restaurant that opened
      downtown?

      Alex: No, I haven't. What's it called?

      Emily: It's called "Savory Bites." They say it has the best pasta in town.

      Alex: That sounds delicious. When are you thinking of checking it out?

      Emily: How about this Saturday? We can make it a dinner date.

      Alex: Sounds like a plan, Emily. I'm looking forward to it.
model-index:
  - name: bart-large-cnn-samsum
    results:
      - task:
          type: summarization
          name: Summarization
        dataset:
          name: >-
            SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive
            Summarization
          type: samsum
        metrics:
          - type: rouge-1
            value: 43.6283
            name: Validation ROUGE-1
          - type: rouge-2
            value: 19.3096
            name: Validation ROUGE-2
          - type: rouge-l
            value: 41.214
            name: Validation ROUGE-L

bart-large-cnn-samsum

This model is a fine-tuned version of facebook/bart-large-cnn on the samsum dataset. It achieves the following results on the evaluation set:

• Loss: 0.755
• Rouge1: 43.6283
• Rouge2: 19.3096
• Rougel: 41.2140
• Rougelsum: 37.2590

Model description

More information needed

Intended uses & limitations

from transformers import pipeline
summarizer = pipeline("summarization", model="AdamCodd/bart-large-cnn-samsum")

conversation = '''Emily: Hey Alex, have you heard about the new restaurant that opened downtown?
Alex: No, I haven't. What's it called?
Emily: It's called "Savory Bites." They say it has the best pasta in town.
Alex: That sounds delicious. When are you thinking of checking it out?
Emily: How about this Saturday? We can make it a dinner date.
Alex: Sounds like a plan, Emily. I'm looking forward to it.                                       
'''
result = summarizer(conversation)
print(result)

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 3e-05
• train_batch_size: 4
• eval_batch_size: 4
• seed: 1270
• optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 150
• num_epochs: 1

Training results

key	value
eval_rouge1	43.6283
eval_rouge2	19.3096
eval_rougeL	41.2140
eval_rougeLsum	37.2590

Framework versions

• Transformers 4.34.0
• Pytorch lightning 2.0.9
• Tokenizers 0.14.0

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