|
--- |
|
license: apache-2.0 |
|
base_model: stevhliu/my_awesome_billsum_model |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- multi_news |
|
metrics: |
|
- rouge |
|
model-index: |
|
- name: my_awesome_multinews_model |
|
results: |
|
- task: |
|
name: Sequence-to-sequence Language Modeling |
|
type: text2text-generation |
|
dataset: |
|
name: multi_news |
|
type: multi_news |
|
config: default |
|
split: validation |
|
args: default |
|
metrics: |
|
- name: Rouge1 |
|
type: rouge |
|
value: 0.1416 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# my_awesome_multinews_model |
|
|
|
This model is a fine-tuned version of [stevhliu/my_awesome_billsum_model](https://huggingface.co/stevhliu/my_awesome_billsum_model) on the multi_news dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 2.8031 |
|
- Rouge1: 0.1416 |
|
- Rouge2: 0.0452 |
|
- Rougel: 0.1098 |
|
- Rougelsum: 0.1099 |
|
- Gen Len: 19.0 |
|
|
|
## 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: 4 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
|
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| |
|
| No log | 1.0 | 282 | 2.8803 | 0.1378 | 0.0427 | 0.1067 | 0.1067 | 19.0 | |
|
| 3.1546 | 2.0 | 564 | 2.8260 | 0.1393 | 0.043 | 0.1077 | 0.1077 | 19.0 | |
|
| 3.1546 | 3.0 | 846 | 2.8089 | 0.1418 | 0.0452 | 0.1096 | 0.1096 | 19.0 | |
|
| 3.0357 | 4.0 | 1128 | 2.8031 | 0.1416 | 0.0452 | 0.1098 | 0.1099 | 19.0 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.35.0 |
|
- Pytorch 2.1.0+cu118 |
|
- Datasets 2.14.6 |
|
- Tokenizers 0.14.1 |
|
|
