---
library_name: transformers
license: apache-2.0
base_model: google/t5-efficient-tiny
tags:
- generated_from_trainer
datasets:
- generator
metrics:
- accuracy
model-index:
- name: salt_language_ID
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: generator
      type: generator
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.608582394590625
---

<!-- 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. -->

# salt_language_ID

This model is a fine-tuned version of [google/t5-efficient-tiny](https://huggingface.co/google/t5-efficient-tiny) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4200
- Accuracy: 0.6086

## 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: 0.001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- training_steps: 20000

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9948        | 0.025 | 500   | 0.7153          | 0.1757   |
| 0.3269        | 0.05  | 1000  | 0.7217          | 0.2611   |
| 0.2853        | 0.075 | 1500  | 0.9151          | 0.2412   |
| 0.1823        | 0.1   | 2000  | 0.5561          | 0.3965   |
| 0.1953        | 0.125 | 2500  | 0.5975          | 0.3824   |
| 0.1831        | 0.15  | 3000  | 0.5670          | 0.4264   |
| 0.141         | 0.175 | 3500  | 0.7885          | 0.3443   |
| 0.1081        | 0.2   | 4000  | 0.8961          | 0.3111   |
| 0.154         | 0.225 | 4500  | 0.7975          | 0.3491   |
| 0.1306        | 0.25  | 5000  | 0.4824          | 0.5092   |
| 0.1013        | 0.275 | 5500  | 0.4946          | 0.4613   |
| 0.1083        | 0.3   | 6000  | 0.6959          | 0.4038   |
| 0.1121        | 0.325 | 6500  | 0.6938          | 0.4004   |
| 0.1168        | 0.35  | 7000  | 0.7787          | 0.3948   |
| 0.1202        | 0.375 | 7500  | 0.5420          | 0.4975   |
| 0.1169        | 0.4   | 8000  | 0.5099          | 0.5128   |
| 0.1119        | 0.425 | 8500  | 0.5815          | 0.4582   |
| 0.1258        | 0.45  | 9000  | 0.5103          | 0.5002   |
| 0.0878        | 0.475 | 9500  | 0.5189          | 0.5089   |
| 0.1032        | 0.5   | 10000 | 0.4365          | 0.5674   |
| 0.0854        | 0.525 | 10500 | 0.5854          | 0.5176   |
| 0.1028        | 0.55  | 11000 | 0.5167          | 0.5253   |
| 0.0853        | 0.575 | 11500 | 0.4268          | 0.5922   |
| 0.0716        | 0.6   | 12000 | 0.5486          | 0.5204   |
| 0.0771        | 0.625 | 12500 | 0.4643          | 0.5532   |
| 0.0613        | 0.65  | 13000 | 0.5525          | 0.5050   |
| 0.0819        | 0.675 | 13500 | 0.4500          | 0.5953   |
| 0.0785        | 0.7   | 14000 | 0.5016          | 0.5245   |
| 0.079         | 0.725 | 14500 | 0.4453          | 0.5789   |
| 0.0749        | 0.75  | 15000 | 0.4218          | 0.5866   |
| 0.0749        | 0.775 | 15500 | 0.4208          | 0.6114   |
| 0.0655        | 0.8   | 16000 | 0.4203          | 0.6133   |
| 0.077         | 0.825 | 16500 | 0.4446          | 0.5891   |
| 0.0516        | 0.85  | 17000 | 0.4239          | 0.5985   |
| 0.0555        | 0.875 | 17500 | 0.4040          | 0.6237   |
| 0.0622        | 0.9   | 18000 | 0.4575          | 0.5978   |
| 0.0752        | 0.925 | 18500 | 0.4257          | 0.5959   |
| 0.0555        | 0.95  | 19000 | 0.4462          | 0.5997   |
| 0.0646        | 0.975 | 19500 | 0.4225          | 0.6124   |
| 0.0676        | 1.0   | 20000 | 0.4200          | 0.6086   |


### Framework versions

- Transformers 4.57.1
- Pytorch 2.8.0+cu126
- Datasets 4.4.0
- Tokenizers 0.22.1