|
--- |
|
license: apache-2.0 |
|
base_model: t5-base |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- rouge |
|
- wer |
|
model-index: |
|
- name: t-5-base-baseline |
|
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. --> |
|
|
|
# t-5-base-baseline |
|
|
|
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 1.1785 |
|
- Rouge1: 0.6772 |
|
- Rouge2: 0.4105 |
|
- Rougel: 0.6161 |
|
- Rougelsum: 0.6161 |
|
- Wer: 0.4869 |
|
- Bleurt: 0.3779 |
|
|
|
## 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: 6 |
|
- eval_batch_size: 6 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 2 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Wer | Bleurt | |
|
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:------:|:------:| |
|
| No log | 0.13 | 250 | 1.3316 | 0.6509 | 0.3768 | 0.5866 | 0.5865 | 0.5217 | 0.3009 | |
|
| 1.7919 | 0.27 | 500 | 1.2776 | 0.6593 | 0.3865 | 0.5962 | 0.5962 | 0.5108 | 0.3009 | |
|
| 1.7919 | 0.4 | 750 | 1.2513 | 0.6633 | 0.3931 | 0.6015 | 0.6014 | 0.5039 | 0.3009 | |
|
| 1.3552 | 0.53 | 1000 | 1.2326 | 0.6667 | 0.3967 | 0.6048 | 0.6047 | 0.5008 | 0.3009 | |
|
| 1.3552 | 0.66 | 1250 | 1.2236 | 0.669 | 0.4 | 0.6072 | 0.6072 | 0.4972 | 0.3314 | |
|
| 1.3074 | 0.8 | 1500 | 1.2118 | 0.6711 | 0.4022 | 0.6093 | 0.6093 | 0.4953 | 0.3314 | |
|
| 1.3074 | 0.93 | 1750 | 1.2022 | 0.6714 | 0.4034 | 0.6105 | 0.6104 | 0.4932 | 0.2798 | |
|
| 1.3037 | 1.06 | 2000 | 1.1972 | 0.673 | 0.4053 | 0.6117 | 0.6116 | 0.4916 | 0.3771 | |
|
| 1.3037 | 1.2 | 2250 | 1.1909 | 0.6749 | 0.4068 | 0.6136 | 0.6135 | 0.4905 | 0.3314 | |
|
| 1.2676 | 1.33 | 2500 | 1.1889 | 0.676 | 0.4086 | 0.6143 | 0.6143 | 0.4893 | 0.3314 | |
|
| 1.2676 | 1.46 | 2750 | 1.1848 | 0.6763 | 0.4091 | 0.615 | 0.6149 | 0.4884 | 0.3314 | |
|
| 1.2796 | 1.6 | 3000 | 1.1829 | 0.677 | 0.4095 | 0.6154 | 0.6154 | 0.488 | 0.3123 | |
|
| 1.2796 | 1.73 | 3250 | 1.1808 | 0.6767 | 0.41 | 0.6157 | 0.6157 | 0.4876 | 0.3779 | |
|
| 1.2489 | 1.86 | 3500 | 1.1787 | 0.6771 | 0.4105 | 0.616 | 0.616 | 0.4869 | 0.3771 | |
|
| 1.2489 | 1.99 | 3750 | 1.1785 | 0.6772 | 0.4105 | 0.6161 | 0.6161 | 0.4869 | 0.3779 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.38.2 |
|
- Pytorch 2.2.1+cu121 |
|
- Datasets 2.18.0 |
|
- Tokenizers 0.15.2 |
|
|
