---
license: apache-2.0
base_model: google/t5-efficient-tiny
tags:
- generated_from_trainer
datasets:
- generator
metrics:
- precision
- recall
- f1
model-index:
- name: salt_language_Classification
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: generator
      type: generator
      config: default
      split: train
      args: default
    metrics:
    - name: Precision
      type: precision
      value: 1.0
    - name: Recall
      type: recall
      value: 1.0
    - name: F1
      type: f1
      value: 1.0
---

<!-- 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_Classification

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: 0.0
- Precision: 1.0
- Recall: 1.0
- F1: 1.0

## 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: 0.001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- training_steps: 20000

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1  |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:---:|
| 0.0           | 0.025 | 500   | 0.0             | 1.0       | 1.0    | 1.0 |
| 0.0           | 0.05  | 1000  | 0.0             | 1.0       | 1.0    | 1.0 |
| 0.0           | 0.075 | 1500  | 0.0             | 1.0       | 1.0    | 1.0 |
| 0.0           | 0.1   | 2000  | 0.0             | 1.0       | 1.0    | 1.0 |
| 0.0           | 0.125 | 2500  | 0.0             | 1.0       | 1.0    | 1.0 |
| 0.0           | 0.15  | 3000  | 0.0             | 1.0       | 1.0    | 1.0 |
| 0.0           | 0.175 | 3500  | 0.0             | 1.0       | 1.0    | 1.0 |
| 0.0           | 0.2   | 4000  | 0.0             | 1.0       | 1.0    | 1.0 |
| 0.0           | 0.225 | 4500  | 0.0             | 1.0       | 1.0    | 1.0 |
| 0.0           | 0.25  | 5000  | 0.0             | 1.0       | 1.0    | 1.0 |
| 0.0           | 0.275 | 5500  | 0.0             | 1.0       | 1.0    | 1.0 |
| 0.0           | 0.3   | 6000  | 0.0             | 1.0       | 1.0    | 1.0 |
| 0.0           | 0.325 | 6500  | 0.0             | 1.0       | 1.0    | 1.0 |
| 0.0           | 0.35  | 7000  | 0.0             | 1.0       | 1.0    | 1.0 |
| 0.0           | 0.375 | 7500  | 0.0             | 1.0       | 1.0    | 1.0 |
| 0.0           | 0.4   | 8000  | 0.0             | 1.0       | 1.0    | 1.0 |
| 0.0           | 0.425 | 8500  | 0.0             | 1.0       | 1.0    | 1.0 |
| 0.0           | 0.45  | 9000  | 0.0             | 1.0       | 1.0    | 1.0 |
| 0.0           | 0.475 | 9500  | 0.0             | 1.0       | 1.0    | 1.0 |
| 0.0           | 0.5   | 10000 | 0.0             | 1.0       | 1.0    | 1.0 |
| 0.0           | 0.525 | 10500 | 0.0             | 1.0       | 1.0    | 1.0 |
| 0.0           | 0.55  | 11000 | 0.0             | 1.0       | 1.0    | 1.0 |
| 0.0           | 0.575 | 11500 | 0.0             | 1.0       | 1.0    | 1.0 |
| 0.0           | 0.6   | 12000 | 0.0             | 1.0       | 1.0    | 1.0 |
| 0.0           | 0.625 | 12500 | 0.0             | 1.0       | 1.0    | 1.0 |
| 0.0           | 0.65  | 13000 | 0.0             | 1.0       | 1.0    | 1.0 |
| 0.0           | 0.675 | 13500 | 0.0             | 1.0       | 1.0    | 1.0 |
| 0.0           | 0.7   | 14000 | 0.0             | 1.0       | 1.0    | 1.0 |
| 0.0           | 0.725 | 14500 | 0.0             | 1.0       | 1.0    | 1.0 |
| 0.0           | 0.75  | 15000 | 0.0             | 1.0       | 1.0    | 1.0 |
| 0.0           | 0.775 | 15500 | 0.0             | 1.0       | 1.0    | 1.0 |
| 0.0           | 0.8   | 16000 | 0.0             | 1.0       | 1.0    | 1.0 |
| 0.0           | 0.825 | 16500 | 0.0             | 1.0       | 1.0    | 1.0 |
| 0.0           | 0.85  | 17000 | 0.0             | 1.0       | 1.0    | 1.0 |
| 0.0           | 0.875 | 17500 | 0.0             | 1.0       | 1.0    | 1.0 |
| 0.0           | 0.9   | 18000 | 0.0             | 1.0       | 1.0    | 1.0 |
| 0.0           | 0.925 | 18500 | 0.0             | 1.0       | 1.0    | 1.0 |
| 0.0           | 0.95  | 19000 | 0.0             | 1.0       | 1.0    | 1.0 |
| 0.0           | 0.975 | 19500 | 0.0             | 1.0       | 1.0    | 1.0 |
| 0.0           | 1.0   | 20000 | 0.0             | 1.0       | 1.0    | 1.0 |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1