metadata
language: en
license: mit
library_name: transformers
tags:
- summarization
- bart
datasets: ccdv/arxiv-summarization
model-index:
- name: BARTxiv
results:
- task:
type: summarization
dataset:
name: arxiv-summarization
type: ccdv/arxiv-summarization
split: validation
metrics:
- type: rouge1
value: 41.70204016592095
- type: rouge2
value: 15.134827404979639
BARTxiv
See the model implementation here.
This model is a fine-tuned version of facebook/bart-large-cnn on the arxiv-summarization dataset. It achieves the following results on the validation set:
- Loss: 0.86
- Rouge1: 41.70
- Rouge2: 15.13
- Rougel: 22.85
- Rougelsum: 37.77
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-6
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adafactor
- num_epochs: 9
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|
| 1.24 | 1.0 | 1073 | 1.24 | 38.32 | 12.80 | 20.55 | 34.50 |
| 1.04 | 2.0 | 2146 | 1.04 | 39.65 | 13.74 | 21.28 | 35.83 |
| 0.979 | 3.0 | 3219 | 0.98 | 40.19 | 14.30 | 21.87 | 36.38 |
| 0.970 | 4.0 | 4292 | 0.97 | 40.87 | 14.44 | 22.14 | 36.89 |
| 0.918 | 5.0 | 5365 | 0.92 | 41.17 | 14.94 | 22.54 | 37.40 |
| 0.901 | 6.0 | 6438 | 0.90 | 41.02 | 14.65 | 22.46 | 37.05 |
| 0.889 | 7.0 | 7511 | 0.89 | 41.32 | 15.09 | 22.64 | 37.42 |
| 0.900 | 8.0 | 8584 | 0 .90 | 41.23 | 15.02 | 22.67 | 37.28 |
| 0.869 | 9.0 | 9657 | 0.87 | 41.70 | 15.13 | 22.85 | 37.77 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.6.1
- Tokenizers 0.13.1