---
license: apache-2.0
base_model: t5-base
tags:
- generated_from_trainer
metrics:
- rouge
- wer
model-index:
- name: t-5-base-bertsum-500
  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-bertsum-500

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: 1.2994
- Rouge1: 0.6466
- Rouge2: 0.3657
- Rougel: 0.5798
- Rougelsum: 0.5798
- Wer: 0.5246
- Bleurt: -0.0784

## 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  | 1.4553          | 0.6223 | 0.3344 | 0.552  | 0.552     | 0.557  | -0.4294 |
| 1.9648        | 0.27  | 500  | 1.3993          | 0.6301 | 0.3443 | 0.5613 | 0.5614    | 0.5467 | -0.4022 |
| 1.9648        | 0.4   | 750  | 1.3747          | 0.6341 | 0.35   | 0.5661 | 0.5661    | 0.5402 | -0.3802 |
| 1.4858        | 0.53  | 1000 | 1.3547          | 0.638  | 0.3533 | 0.5693 | 0.5693    | 0.5378 | -0.0447 |
| 1.4858        | 0.66  | 1250 | 1.3431          | 0.639  | 0.3559 | 0.5715 | 0.5715    | 0.5342 | -0.0292 |
| 1.4484        | 0.8   | 1500 | 1.3321          | 0.6406 | 0.3578 | 0.573  | 0.573     | 0.5322 | -0.0292 |
| 1.4484        | 0.93  | 1750 | 1.3238          | 0.6418 | 0.3593 | 0.5747 | 0.5747    | 0.5306 | -0.0784 |
| 1.4226        | 1.06  | 2000 | 1.3185          | 0.6433 | 0.3616 | 0.5762 | 0.5762    | 0.5281 | -0.1084 |
| 1.4226        | 1.2   | 2250 | 1.3131          | 0.6442 | 0.3624 | 0.5775 | 0.5775    | 0.5277 | -0.1084 |
| 1.3917        | 1.33  | 2500 | 1.3102          | 0.6453 | 0.3638 | 0.5783 | 0.5783    | 0.5266 | -0.0784 |
| 1.3917        | 1.46  | 2750 | 1.3060          | 0.6458 | 0.3641 | 0.5788 | 0.5788    | 0.5256 | -0.0292 |
| 1.4048        | 1.6   | 3000 | 1.3040          | 0.6461 | 0.3649 | 0.5792 | 0.5792    | 0.5253 | -0.0784 |
| 1.4048        | 1.73  | 3250 | 1.3015          | 0.6463 | 0.3653 | 0.5796 | 0.5795    | 0.525  | -0.0292 |
| 1.3803        | 1.86  | 3500 | 1.2999          | 0.6463 | 0.3654 | 0.5795 | 0.5795    | 0.5247 | -0.0784 |
| 1.3803        | 1.99  | 3750 | 1.2994          | 0.6466 | 0.3657 | 0.5798 | 0.5798    | 0.5246 | -0.0784 |


### Framework versions

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