---
license: apache-2.0
base_model: t5-base
tags:
- generated_from_trainer
metrics:
- rouge
- wer
model-index:
- name: t-5-base-abs2abs
  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-abs2abs

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: 1.3203
- Rouge1: 0.6447
- Rouge2: 0.3627
- Rougel: 0.5772
- Rougelsum: 0.5774
- Wer: 0.5292
- Bleurt: -0.1862

## 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  | 1.4708          | 0.6226 | 0.3343 | 0.5515 | 0.5515    | 0.559  | -0.1681 |
| 1.9361        | 0.27  | 500  | 1.4181          | 0.6277 | 0.3424 | 0.559  | 0.559     | 0.5498 | -0.1527 |
| 1.9361        | 0.41  | 750  | 1.3918          | 0.6327 | 0.3469 | 0.5633 | 0.5634    | 0.5453 | -0.1653 |
| 1.5072        | 0.55  | 1000 | 1.3740          | 0.6353 | 0.3509 | 0.5664 | 0.5665    | 0.541  | -0.1653 |
| 1.5072        | 0.68  | 1250 | 1.3602          | 0.637  | 0.3529 | 0.5686 | 0.5687    | 0.539  | -0.4817 |
| 1.4761        | 0.82  | 1500 | 1.3504          | 0.639  | 0.3557 | 0.5711 | 0.5712    | 0.5361 | -0.1653 |
| 1.4761        | 0.96  | 1750 | 1.3424          | 0.64   | 0.3573 | 0.5727 | 0.5728    | 0.5341 | -0.1653 |
| 1.4475        | 1.09  | 2000 | 1.3368          | 0.6414 | 0.3587 | 0.5736 | 0.5738    | 0.5329 | -0.4817 |
| 1.4475        | 1.23  | 2250 | 1.3324          | 0.6423 | 0.36   | 0.5748 | 0.575     | 0.5316 | -0.4726 |
| 1.4375        | 1.36  | 2500 | 1.3280          | 0.6436 | 0.3609 | 0.5757 | 0.5758    | 0.5309 | -0.3069 |
| 1.4375        | 1.5   | 2750 | 1.3246          | 0.6442 | 0.3618 | 0.5765 | 0.5766    | 0.5304 | -0.1862 |
| 1.4053        | 1.64  | 3000 | 1.3222          | 0.6445 | 0.3622 | 0.5769 | 0.577     | 0.5296 | -0.1862 |
| 1.4053        | 1.77  | 3250 | 1.3208          | 0.6448 | 0.3626 | 0.5771 | 0.5772    | 0.5293 | -0.1862 |
| 1.3911        | 1.91  | 3500 | 1.3203          | 0.6447 | 0.3627 | 0.5772 | 0.5774    | 0.5292 | -0.1862 |


### Framework versions

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