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---
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](https://huggingface.co/facebook/bart-large-cnn) on the [samsum dataset](https://huggingface.co/datasets/samsum).
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
```python
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
If you want to support me, you can [here](https://ko-fi.com/adamcodd). |