|
--- |
|
license: mit |
|
base_model: facebook/bart-large-cnn |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- rouge |
|
model-index: |
|
- name: pep_summarization |
|
results: [] |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# pep_summarization |
|
|
|
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.1242 |
|
- Rouge1: 75.3806 |
|
- Rouge2: 74.6735 |
|
- Rougel: 75.5866 |
|
- Rougelsum: 75.5446 |
|
- Gen Len: 85.3188 |
|
|
|
## 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-05 |
|
- train_batch_size: 4 |
|
- eval_batch_size: 4 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 5.0 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
|
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| |
|
| No log | 1.0 | 69 | 0.0957 | 72.6601 | 71.6824 | 72.6858 | 72.4668 | 95.4493 | |
|
| No log | 2.0 | 138 | 0.1345 | 75.0063 | 74.0782 | 75.0597 | 74.8943 | 92.0145 | |
|
| No log | 3.0 | 207 | 0.1412 | 75.3012 | 74.5492 | 75.4246 | 75.324 | 85.4638 | |
|
| No log | 4.0 | 276 | 0.1089 | 74.8426 | 74.0317 | 74.8939 | 74.8128 | 85.0435 | |
|
| No log | 5.0 | 345 | 0.1242 | 75.3806 | 74.6735 | 75.5866 | 75.5446 | 85.3188 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.38.0.dev0 |
|
- Pytorch 2.1.2+cu121 |
|
- Datasets 2.16.1 |
|
- Tokenizers 0.15.0 |
|
|