---
license: apache-2.0
tags:
- generated_from_trainer
base_model: t5-small
model-index:
- name: results_t5small
  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. -->

# results_t5small

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: 0.0050

## 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: 0.005
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.0235        | 0.12  | 500   | 0.0144          |
| 0.0151        | 0.25  | 1000  | 0.0119          |
| 0.0153        | 0.37  | 1500  | 0.0101          |
| 0.0173        | 0.5   | 2000  | 0.0109          |
| 0.0132        | 0.62  | 2500  | 0.0100          |
| 0.0123        | 0.75  | 3000  | 0.0092          |
| 0.0136        | 0.87  | 3500  | 0.0110          |
| 0.014         | 0.99  | 4000  | 0.0089          |
| 0.0123        | 1.12  | 4500  | 0.0094          |
| 0.0155        | 1.24  | 5000  | 0.0088          |
| 0.0119        | 1.37  | 5500  | 0.0095          |
| 0.0126        | 1.49  | 6000  | 0.0089          |
| 0.0111        | 1.61  | 6500  | 0.0083          |
| 0.0129        | 1.74  | 7000  | 0.0081          |
| 0.0115        | 1.86  | 7500  | 0.0087          |
| 0.0133        | 1.99  | 8000  | 0.0085          |
| 0.0167        | 2.11  | 8500  | 0.0105          |
| 0.0113        | 2.24  | 9000  | 0.0088          |
| 0.0106        | 2.36  | 9500  | 0.0077          |
| 0.0101        | 2.48  | 10000 | 0.0075          |
| 0.0086        | 2.61  | 10500 | 0.0076          |
| 0.0112        | 2.73  | 11000 | 0.0075          |
| 0.0097        | 2.86  | 11500 | 0.0076          |
| 0.0093        | 2.98  | 12000 | 0.0078          |
| 0.0086        | 3.1   | 12500 | 0.0076          |
| 0.0092        | 3.23  | 13000 | 0.0077          |
| 0.0089        | 3.35  | 13500 | 0.0074          |
| 0.0086        | 3.48  | 14000 | 0.0072          |
| 0.0084        | 3.6   | 14500 | 0.0072          |
| 0.0083        | 3.73  | 15000 | 0.0073          |
| 0.0076        | 3.85  | 15500 | 0.0072          |
| 0.0097        | 3.97  | 16000 | 0.0071          |
| 0.0074        | 4.1   | 16500 | 0.0071          |
| 0.0074        | 4.22  | 17000 | 0.0070          |
| 0.0089        | 4.35  | 17500 | 0.0069          |
| 0.0072        | 4.47  | 18000 | 0.0069          |
| 0.0072        | 4.6   | 18500 | 0.0068          |
| 0.0084        | 4.72  | 19000 | 0.0067          |
| 0.0076        | 4.84  | 19500 | 0.0072          |
| 0.0084        | 4.97  | 20000 | 0.0067          |
| 0.0062        | 5.09  | 20500 | 0.0065          |
| 0.007         | 5.22  | 21000 | 0.0064          |
| 0.0075        | 5.34  | 21500 | 0.0063          |
| 0.0067        | 5.46  | 22000 | 0.0064          |
| 0.0063        | 5.59  | 22500 | 0.0065          |
| 0.007         | 5.71  | 23000 | 0.0062          |
| 0.0064        | 5.84  | 23500 | 0.0062          |
| 0.0073        | 5.96  | 24000 | 0.0062          |
| 0.0064        | 6.09  | 24500 | 0.0063          |
| 0.0059        | 6.21  | 25000 | 0.0060          |
| 0.0062        | 6.33  | 25500 | 0.0060          |
| 0.0057        | 6.46  | 26000 | 0.0060          |
| 0.006         | 6.58  | 26500 | 0.0058          |
| 0.0053        | 6.71  | 27000 | 0.0058          |
| 0.0064        | 6.83  | 27500 | 0.0057          |
| 0.0061        | 6.95  | 28000 | 0.0057          |
| 0.0055        | 7.08  | 28500 | 0.0057          |
| 0.0065        | 7.2   | 29000 | 0.0057          |
| 0.0058        | 7.33  | 29500 | 0.0057          |
| 0.0052        | 7.45  | 30000 | 0.0055          |
| 0.0058        | 7.58  | 30500 | 0.0056          |
| 0.0055        | 7.7   | 31000 | 0.0055          |
| 0.0049        | 7.82  | 31500 | 0.0054          |
| 0.0053        | 7.95  | 32000 | 0.0053          |
| 0.005         | 8.07  | 32500 | 0.0054          |
| 0.0051        | 8.2   | 33000 | 0.0053          |
| 0.0047        | 8.32  | 33500 | 0.0053          |
| 0.0057        | 8.45  | 34000 | 0.0052          |
| 0.0049        | 8.57  | 34500 | 0.0053          |
| 0.0051        | 8.69  | 35000 | 0.0052          |
| 0.0047        | 8.82  | 35500 | 0.0052          |
| 0.0051        | 8.94  | 36000 | 0.0051          |
| 0.0044        | 9.07  | 36500 | 0.0052          |
| 0.0042        | 9.19  | 37000 | 0.0052          |
| 0.0045        | 9.31  | 37500 | 0.0051          |
| 0.0043        | 9.44  | 38000 | 0.0051          |
| 0.0041        | 9.56  | 38500 | 0.0051          |
| 0.0042        | 9.69  | 39000 | 0.0051          |
| 0.004         | 9.81  | 39500 | 0.0050          |
| 0.0038        | 9.94  | 40000 | 0.0050          |


### Framework versions

- Transformers 4.37.1
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.1