|
--- |
|
license: apache-2.0 |
|
tags: |
|
- summarization |
|
- generated_from_trainer |
|
metrics: |
|
- rouge |
|
model-index: |
|
- name: t5-small-finetuned-multi-news |
|
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. --> |
|
|
|
# t5-small-finetuned-multi-news |
|
|
|
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 2.8049 |
|
- Rouge1: 15.1241 |
|
- Rouge2: 4.9514 |
|
- Rougel: 11.5019 |
|
- Rougelsum: 13.3079 |
|
|
|
## 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: 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: 7 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |
|
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:| |
|
| 3.2411 | 1.0 | 1250 | 2.8772 | 14.6774 | 4.7697 | 11.335 | 13.0082 | |
|
| 3.079 | 2.0 | 2500 | 2.8438 | 14.9558 | 4.8748 | 11.4023 | 13.2198 | |
|
| 3.0257 | 3.0 | 3750 | 2.8240 | 15.133 | 4.9814 | 11.572 | 13.3607 | |
|
| 2.9903 | 4.0 | 5000 | 2.8153 | 15.1339 | 4.9123 | 11.5038 | 13.3464 | |
|
| 2.9659 | 5.0 | 6250 | 2.8085 | 15.1134 | 5.0057 | 11.5478 | 13.3483 | |
|
| 2.9461 | 6.0 | 7500 | 2.8066 | 15.154 | 4.9641 | 11.5276 | 13.3523 | |
|
| 2.936 | 7.0 | 8750 | 2.8049 | 15.1241 | 4.9514 | 11.5019 | 13.3079 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.28.1 |
|
- Pytorch 2.0.0+cu118 |
|
- Datasets 2.11.0 |
|
- Tokenizers 0.13.3 |
|
|