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---
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