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AdamCodd
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bart-large-xsum-samsum

Summarization
Transformers
ONNX
Safetensors
bart
feature-extraction
Generated from Trainer
seq2seq
Eval Results
Inference Endpoints
Model card Files Community
1
bart-large-xsum-samsum
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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-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](https://huggingface.co/facebook/bart-large-xsum) on the [samsum dataset](https://huggingface.co/datasets/samsum).
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](https://huggingface.co/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

If you want to support me, you can [here](https://ko-fi.com/adamcodd).