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.

python

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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