transformers pytorch bart text2text-generation dataset:ccdv/arxiv-summarization model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- ccdv/arxiv-summarization
model-index:
- name: distilbart-sum-arxiv
  results:
  - task:
      type: summarization
      name: Summarization
    dataset:
      name: arxiv-summarization
      type: arxiv-summarization
    metrics:
    - type: rouge-1
      value: 42.1856
      name: Validation ROUGE-1
    - type: rouge-2
      value: 15.4815
      name: Validation ROUGE-2
    - type: rouge-l
      value: 24.4409
      name: Validation ROUGE-L
---
## distilbart-sum-arxiv
This model is a fine-tuned version of [sshleifer/distilbart-xsum-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on a subset of the [ccdv/arxiv-summarization dataset](https://huggingface.co/datasets/ccdv/arxiv-summarization). It achieves the following results on the evaluation set:
* Loss: 2.420
* Rouge1: 42.185
* Rouge2: 15.481
* RougeL: 24.440
* RougeLSum: 24.260

## Model description
This model is a distilled version of BART with 306M parameters (vs. 406 for the BART model), but it is 1.68 times faster than BART at inference. It has been trained on 60_000 samples and has a limitation of 1024 tokens. 

## Intended uses & limitations
Since this model has been trained on scientific papers, it may perform poorly when attempting to summarize other types of content.

```python
from transformers import pipeline
summarizer = pipeline("text2text-generation", model="AdamCodd/distilbart-sum-arxiv")
paper = "Scientific paper..."
result = summarizer(paper)
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 | 42.185638427734375 |
| eval_rouge2 | 15.481599807739258 |
| eval_rougeL | 24.440900802612305 |
| eval_rougeLsum | 24.260608673095703 |

### Framework versions

- Transformers 4.33.0
- Pytorch lightning 2.0.8
- Tokenizers 0.13.3

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