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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