|
--- |
|
license: apache-2.0 |
|
base_model: sshleifer/distilbart-cnn-6-6 |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- rouge |
|
model-index: |
|
- name: plain-bart-on-presummarized-2-clusters-wcep |
|
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. --> |
|
|
|
# plain-bart-on-presummarized-2-clusters-wcep |
|
|
|
This model is a fine-tuned version of [sshleifer/distilbart-cnn-6-6](https://huggingface.co/sshleifer/distilbart-cnn-6-6) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 2.0775 |
|
- Rouge1: 36.3774 |
|
- Rouge2: 15.2074 |
|
- Rougel: 25.7706 |
|
- Rougelsum: 29.2593 |
|
- Gen Len: 67.6608 |
|
|
|
## 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: 16 |
|
- eval_batch_size: 16 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 3 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
|
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| |
|
| 2.2178 | 1.0 | 510 | 2.0873 | 36.3079 | 15.0162 | 25.5837 | 29.129 | 67.8461 | |
|
| 1.8901 | 2.0 | 1020 | 2.0696 | 36.0914 | 15.0005 | 25.6729 | 29.2956 | 68.3451 | |
|
| 1.7267 | 3.0 | 1530 | 2.0775 | 36.3774 | 15.2074 | 25.7706 | 29.2593 | 67.6608 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.39.3 |
|
- Pytorch 2.1.2 |
|
- Datasets 2.18.0 |
|
- Tokenizers 0.15.2 |
|
|
