|
--- |
|
license: apache-2.0 |
|
tags: |
|
- summarization |
|
- generated_from_trainer |
|
datasets: |
|
- cnn_dailymail |
|
metrics: |
|
- rouge |
|
model-index: |
|
- name: bart-base-finetuned-cnn_dailymail |
|
results: |
|
- task: |
|
name: Sequence-to-sequence Language Modeling |
|
type: text2text-generation |
|
dataset: |
|
name: cnn_dailymail |
|
type: cnn_dailymail |
|
config: 3.0.0 |
|
split: train |
|
args: 3.0.0 |
|
metrics: |
|
- name: Rouge1 |
|
type: rouge |
|
value: 0.35105989316705805 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# bart-base-finetuned-cnn_dailymail |
|
|
|
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the cnn_dailymail dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 1.5396 |
|
- Rouge1: 0.3511 |
|
- Rouge2: 0.1925 |
|
- Rougel: 0.3086 |
|
- Rougelsum: 0.3292 |
|
|
|
## 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: 5.6e-05 |
|
- train_batch_size: 8 |
|
- eval_batch_size: 8 |
|
- 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 | |
|
|:-------------:|:-----:|:------:|:---------------:|:------:|:------:|:------:|:---------:| |
|
| 1.9486 | 1.0 | 35890 | 1.5941 | 0.3498 | 0.1893 | 0.3063 | 0.3272 | |
|
| 1.6706 | 2.0 | 71780 | 1.5601 | 0.3503 | 0.1916 | 0.3079 | 0.3279 | |
|
| 1.4809 | 3.0 | 107670 | 1.5423 | 0.3520 | 0.1923 | 0.3086 | 0.3295 | |
|
| 1.3293 | 4.0 | 143560 | 1.5396 | 0.3511 | 0.1925 | 0.3086 | 0.3292 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.24.0 |
|
- Pytorch 1.12.1+cu113 |
|
- Datasets 2.7.1 |
|
- Tokenizers 0.13.2 |
|
|
