---
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: 1.1670
- Rouge1: 0.6791
- Rouge2: 0.4136
- Rougel: 0.6183
- Rougelsum: 0.6185
- Wer: 0.4846
- Bleurt: 0.3314

## 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        | 0.14  | 250  | 1.3197          | 0.6548 | 0.3804 | 0.5904 | 0.5905    | 0.5188 | 0.3009 |
| 1.7026        | 0.27  | 500  | 1.2676          | 0.6613 | 0.3904 | 0.5985 | 0.5987    | 0.5074 | 0.3009 |
| 1.7026        | 0.41  | 750  | 1.2385          | 0.6664 | 0.3961 | 0.6043 | 0.6044    | 0.5023 | 0.3009 |
| 1.3446        | 0.55  | 1000 | 1.2234          | 0.6691 | 0.4009 | 0.6075 | 0.6076    | 0.4972 | 0.3009 |
| 1.3446        | 0.68  | 1250 | 1.2089          | 0.671  | 0.4031 | 0.6099 | 0.6101    | 0.4944 | 0.3009 |
| 1.309         | 0.82  | 1500 | 1.1983          | 0.673  | 0.4064 | 0.6121 | 0.6123    | 0.4914 | 0.3009 |
| 1.309         | 0.96  | 1750 | 1.1900          | 0.6744 | 0.4075 | 0.6136 | 0.6137    | 0.4897 | 0.3009 |
| 1.2783        | 1.09  | 2000 | 1.1840          | 0.6744 | 0.4082 | 0.614  | 0.6141    | 0.4889 | 0.2798 |
| 1.2783        | 1.23  | 2250 | 1.1808          | 0.6759 | 0.4102 | 0.6154 | 0.6156    | 0.4875 | 0.2944 |
| 1.2683        | 1.36  | 2500 | 1.1763          | 0.6771 | 0.4111 | 0.6163 | 0.6165    | 0.4863 | 0.3153 |
| 1.2683        | 1.5   | 2750 | 1.1720          | 0.6772 | 0.4115 | 0.6169 | 0.617     | 0.4859 | 0.4109 |
| 1.2469        | 1.64  | 3000 | 1.1701          | 0.6783 | 0.4127 | 0.6176 | 0.6178    | 0.485  | 0.3314 |
| 1.2469        | 1.77  | 3250 | 1.1680          | 0.6786 | 0.4131 | 0.6178 | 0.6179    | 0.4849 | 0.3314 |
| 1.2171        | 1.91  | 3500 | 1.1670          | 0.6791 | 0.4136 | 0.6183 | 0.6185    | 0.4846 | 0.3314 |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2