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-xsum-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: 54.3073
            name: Validation ROUGE-1
          - type: rouge-2
            value: 29.0947
            name: Validation ROUGE-2
          - type: rouge-l
            value: 44.4676
            name: Validation ROUGE-L

bart-large-xsum-samsum

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

Loss: 0.759
Rouge1: 54.3073
Rouge2: 29.0947
Rougel: 44.4676
Rougelsum: 49.895

Model description

This model tends to generate less verbose summaries compared to AdamCodd/bart-large-cnn-samsum, yet I find its quality to be superior (which is reflected in the metrics).

Intended uses & limitations

Suitable for summarizing dialogue-style text, it may not perform as well with other types of text formats.

from transformers import pipeline
summarizer = pipeline("summarization", model="AdamCodd/bart-large-xsum-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	54.3073
eval_rouge2	29.0947
eval_rougeL	44.4676
eval_rougeLsum	49.895

Framework versions

Transformers 4.35.0
Accelerate 0.24.1
Datasets 2.14.6
Tokenizers 0.14.3

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