|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- multi_news |
|
metrics: |
|
- rouge |
|
model-index: |
|
- name: t5-small-finetuned-multi-news |
|
results: |
|
- task: |
|
name: Sequence-to-sequence Language Modeling |
|
type: text2text-generation |
|
dataset: |
|
name: multi_news |
|
type: multi_news |
|
args: default |
|
metrics: |
|
- name: Rouge1 |
|
type: rouge |
|
value: 14.5549 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# t5-small-finetuned-multi-news |
|
|
|
This model is a fine-tuned version of [t5-small](https://huggingface.co./t5-small) on the multi_news dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 2.7775 |
|
- Rouge1: 14.5549 |
|
- Rouge2: 4.5934 |
|
- Rougel: 11.1178 |
|
- Rougelsum: 12.8964 |
|
- 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: 8 |
|
- eval_batch_size: 8 |
|
- seed: 42 |
|
- gradient_accumulation_steps: 4 |
|
- total_train_batch_size: 32 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 1 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
|
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| |
|
| 3.0211 | 1.0 | 1405 | 2.7775 | 14.5549 | 4.5934 | 11.1178 | 12.8964 | 19.0 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.17.0 |
|
- Pytorch 1.10.0+cu111 |
|
- Datasets 2.0.0 |
|
- Tokenizers 0.11.6 |
|
|
