metadata
license: mit
tags:
- generated_from_trainer
datasets:
- billsum
metrics:
- rouge
model-index:
- name: bart_summarization_pretrained
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: billsum
type: billsum
config: default
split: ca_test
args: default
metrics:
- name: Rouge1
type: rouge
value: 0.5264
bart_summarization_pretrained
This model is a fine-tuned version of facebook/bart-large-cnn on the billsum dataset. It achieves the following results on the evaluation set:
- Loss: 1.7402
- Rouge1: 0.5264
- Rouge2: 0.2745
- Rougel: 0.3432
- Rougelsum: 0.4049
- Gen Len: 131.0645
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: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 1.7347 | 1.0 | 989 | 1.6263 | 0.5044 | 0.254 | 0.3219 | 0.3734 | 121.8306 |
| 1.2029 | 2.0 | 1978 | 1.6037 | 0.5278 | 0.2723 | 0.3351 | 0.3977 | 136.4718 |
| 0.8435 | 3.0 | 2967 | 1.6054 | 0.513 | 0.2661 | 0.3357 | 0.3957 | 129.1048 |
| 0.6326 | 4.0 | 3956 | 1.7402 | 0.5264 | 0.2745 | 0.3432 | 0.4049 | 131.0645 |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3