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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-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: 0.6446
- Rouge2: 0.3626
- Rougel: 0.5773
- Rougelsum: 0.5771
- 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 | 0.5514 | 0.5512 | 0.559 | -0.1681 |
| 1.9361 | 0.27 | 500 | 1.4181 | 0.6277 | 0.3422 | 0.5591 | 0.5588 | 0.5498 | -0.1527 |
| 1.9361 | 0.41 | 750 | 1.3918 | 0.6326 | 0.3467 | 0.5633 | 0.5632 | 0.5453 | -0.1653 |
| 1.5072 | 0.55 | 1000 | 1.3740 | 0.6352 | 0.3508 | 0.5664 | 0.5662 | 0.541 | -0.1653 |
| 1.5072 | 0.68 | 1250 | 1.3602 | 0.6369 | 0.3528 | 0.5687 | 0.5685 | 0.539 | -0.4817 |
| 1.4761 | 0.82 | 1500 | 1.3504 | 0.6388 | 0.3557 | 0.5711 | 0.571 | 0.5361 | -0.1653 |
| 1.4761 | 0.96 | 1750 | 1.3424 | 0.6399 | 0.3573 | 0.5728 | 0.5725 | 0.5341 | -0.1653 |
| 1.4475 | 1.09 | 2000 | 1.3368 | 0.6413 | 0.3586 | 0.5737 | 0.5735 | 0.5329 | -0.4817 |
| 1.4475 | 1.23 | 2250 | 1.3324 | 0.6422 | 0.36 | 0.5748 | 0.5746 | 0.5316 | -0.4726 |
| 1.4375 | 1.36 | 2500 | 1.3280 | 0.6435 | 0.3608 | 0.5757 | 0.5754 | 0.5309 | -0.3069 |
| 1.4375 | 1.5 | 2750 | 1.3246 | 0.644 | 0.3618 | 0.5765 | 0.5763 | 0.5304 | -0.1862 |
| 1.4053 | 1.64 | 3000 | 1.3222 | 0.6443 | 0.3622 | 0.5769 | 0.5767 | 0.5296 | -0.1862 |
| 1.4053 | 1.77 | 3250 | 1.3208 | 0.6446 | 0.3625 | 0.5771 | 0.5769 | 0.5293 | -0.1862 |
| 1.3911 | 1.91 | 3500 | 1.3203 | 0.6446 | 0.3626 | 0.5773 | 0.5771 | 0.5292 | -0.1862 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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