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license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: my_awesome_sumarize_model
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. -->
# my_awesome_sumarize_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2464
- Rouge1: 0.3573
- Rouge2: 0.2493
- Rougel: 0.3411
- Rougelsum: 0.3387
- Gen Len: 19.0
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 4 | 1.2873 | 0.3626 | 0.2514 | 0.3512 | 0.3486 | 19.0 |
| No log | 2.0 | 8 | 1.2838 | 0.3542 | 0.2441 | 0.3452 | 0.3428 | 19.0 |
| No log | 3.0 | 12 | 1.2756 | 0.3542 | 0.2441 | 0.3452 | 0.3428 | 19.0 |
| No log | 4.0 | 16 | 1.2679 | 0.3542 | 0.2441 | 0.3452 | 0.3428 | 19.0 |
| No log | 5.0 | 20 | 1.2627 | 0.3542 | 0.2441 | 0.3452 | 0.3428 | 19.0 |
| No log | 6.0 | 24 | 1.2608 | 0.3542 | 0.2441 | 0.3452 | 0.3428 | 19.0 |
| No log | 7.0 | 28 | 1.2587 | 0.3542 | 0.2441 | 0.3452 | 0.3428 | 19.0 |
| No log | 8.0 | 32 | 1.2576 | 0.359 | 0.2495 | 0.346 | 0.3428 | 19.0 |
| No log | 9.0 | 36 | 1.2569 | 0.359 | 0.2495 | 0.346 | 0.3428 | 19.0 |
| No log | 10.0 | 40 | 1.2558 | 0.359 | 0.2495 | 0.346 | 0.3428 | 19.0 |
| No log | 11.0 | 44 | 1.2537 | 0.359 | 0.2495 | 0.346 | 0.3428 | 19.0 |
| No log | 12.0 | 48 | 1.2521 | 0.3573 | 0.2493 | 0.3411 | 0.3387 | 19.0 |
| No log | 13.0 | 52 | 1.2500 | 0.3573 | 0.2493 | 0.3411 | 0.3387 | 19.0 |
| No log | 14.0 | 56 | 1.2486 | 0.3573 | 0.2493 | 0.3411 | 0.3387 | 19.0 |
| No log | 15.0 | 60 | 1.2476 | 0.3573 | 0.2493 | 0.3411 | 0.3387 | 19.0 |
| No log | 16.0 | 64 | 1.2474 | 0.3573 | 0.2493 | 0.3411 | 0.3387 | 19.0 |
| No log | 17.0 | 68 | 1.2468 | 0.3573 | 0.2493 | 0.3411 | 0.3387 | 19.0 |
| No log | 18.0 | 72 | 1.2465 | 0.3573 | 0.2493 | 0.3411 | 0.3387 | 19.0 |
| No log | 19.0 | 76 | 1.2463 | 0.3573 | 0.2493 | 0.3411 | 0.3387 | 19.0 |
| No log | 20.0 | 80 | 1.2464 | 0.3573 | 0.2493 | 0.3411 | 0.3387 | 19.0 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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