---
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: 0.6774
- Rouge2: 0.4106
- Rougel: 0.6163
- 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          | 0.6511 | 0.3769 | 0.5868 | 0.5866    | 0.5217 | 0.3009 |
| 1.7919        | 0.27  | 500  | 1.2776          | 0.6595 | 0.3866 | 0.5964 | 0.5962    | 0.5108 | 0.3009 |
| 1.7919        | 0.4   | 750  | 1.2513          | 0.6635 | 0.3932 | 0.6016 | 0.6014    | 0.5039 | 0.3009 |
| 1.3552        | 0.53  | 1000 | 1.2326          | 0.6668 | 0.3968 | 0.605  | 0.6048    | 0.5008 | 0.3009 |
| 1.3552        | 0.66  | 1250 | 1.2236          | 0.6692 | 0.4    | 0.6073 | 0.6072    | 0.4972 | 0.3314 |
| 1.3074        | 0.8   | 1500 | 1.2118          | 0.6713 | 0.4023 | 0.6094 | 0.6093    | 0.4953 | 0.3314 |
| 1.3074        | 0.93  | 1750 | 1.2022          | 0.6716 | 0.4035 | 0.6106 | 0.6105    | 0.4932 | 0.2798 |
| 1.3037        | 1.06  | 2000 | 1.1972          | 0.6731 | 0.4053 | 0.6118 | 0.6117    | 0.4916 | 0.3771 |
| 1.3037        | 1.2   | 2250 | 1.1909          | 0.675  | 0.4069 | 0.6136 | 0.6135    | 0.4905 | 0.3314 |
| 1.2676        | 1.33  | 2500 | 1.1889          | 0.6761 | 0.4087 | 0.6144 | 0.6143    | 0.4893 | 0.3314 |
| 1.2676        | 1.46  | 2750 | 1.1848          | 0.6764 | 0.4091 | 0.6151 | 0.615     | 0.4884 | 0.3314 |
| 1.2796        | 1.6   | 3000 | 1.1829          | 0.6771 | 0.4096 | 0.6156 | 0.6154    | 0.488  | 0.3123 |
| 1.2796        | 1.73  | 3250 | 1.1808          | 0.6769 | 0.4101 | 0.6159 | 0.6158    | 0.4876 | 0.3779 |
| 1.2489        | 1.86  | 3500 | 1.1787          | 0.6772 | 0.4106 | 0.6162 | 0.6161    | 0.4869 | 0.3771 |
| 1.2489        | 1.99  | 3750 | 1.1785          | 0.6774 | 0.4106 | 0.6163 | 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