transformers tensorboard safetensors t5 text-classification generated_from_trainer dataset:generator base_model:google/t5-efficient-tiny base_model:finetune:google/t5-efficient-tiny license:apache-2.0 model-index autotrain_compatible text-generation-inference endpoints_compatible region:us
---
base_model: google/t5-efficient-tiny
datasets:
- generator
license: apache-2.0
metrics:
- accuracy
- precision
- recall
- f1
tags:
- generated_from_trainer
model-index:
- name: salt_language_Classification
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: generator
      type: generator
      config: default
      split: train
      args: default
    metrics:
    - type: accuracy
      value: 0.9781586021505376
      name: Accuracy
    - type: precision
      value: 0.9786579334649282
      name: Precision
    - type: recall
      value: 0.9781586021505376
      name: Recall
    - type: f1
      value: 0.97818824673623
      name: F1
---

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# 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: 0.0615
- Accuracy: 0.9782
- Precision: 0.9787
- Recall: 0.9782
- F1: 0.9782

## 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 | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.2011        | 0.025 | 500   | 0.4979          | 0.8733   | 0.9001    | 0.8733 | 0.8714 |
| 0.234         | 0.05  | 1000  | 0.1886          | 0.9345   | 0.9354    | 0.9345 | 0.9345 |
| 0.2083        | 0.075 | 1500  | 0.1833          | 0.9328   | 0.9391    | 0.9328 | 0.9328 |
| 0.1838        | 0.1   | 2000  | 0.1457          | 0.9476   | 0.9479    | 0.9476 | 0.9475 |
| 0.1737        | 0.125 | 2500  | 0.1659          | 0.9409   | 0.9438    | 0.9409 | 0.9411 |
| 0.1591        | 0.15  | 3000  | 0.1450          | 0.9516   | 0.9524    | 0.9516 | 0.9517 |
| 0.1571        | 0.175 | 3500  | 0.1351          | 0.9459   | 0.9485    | 0.9459 | 0.9461 |
| 0.1513        | 0.2   | 4000  | 0.1510          | 0.9456   | 0.9515    | 0.9456 | 0.9460 |
| 0.1439        | 0.225 | 4500  | 0.1339          | 0.9546   | 0.9578    | 0.9546 | 0.9547 |
| 0.1394        | 0.25  | 5000  | 0.1052          | 0.9657   | 0.9658    | 0.9657 | 0.9656 |
| 0.1472        | 0.275 | 5500  | 0.1088          | 0.9610   | 0.9629    | 0.9610 | 0.9609 |
| 0.1385        | 0.3   | 6000  | 0.0792          | 0.9694   | 0.9696    | 0.9694 | 0.9694 |
| 0.1349        | 0.325 | 6500  | 0.1063          | 0.9610   | 0.9632    | 0.9610 | 0.9613 |
| 0.1215        | 0.35  | 7000  | 0.0855          | 0.9688   | 0.9694    | 0.9688 | 0.9687 |
| 0.133         | 0.375 | 7500  | 0.1049          | 0.9630   | 0.9640    | 0.9630 | 0.9630 |
| 0.1226        | 0.4   | 8000  | 0.0938          | 0.9667   | 0.9675    | 0.9667 | 0.9667 |
| 0.1222        | 0.425 | 8500  | 0.1134          | 0.9570   | 0.9604    | 0.9570 | 0.9573 |
| 0.1165        | 0.45  | 9000  | 0.0997          | 0.9688   | 0.9697    | 0.9688 | 0.9687 |
| 0.1174        | 0.475 | 9500  | 0.1002          | 0.9661   | 0.9680    | 0.9661 | 0.9659 |
| 0.1165        | 0.5   | 10000 | 0.0807          | 0.9728   | 0.9728    | 0.9728 | 0.9728 |
| 0.1065        | 0.525 | 10500 | 0.0750          | 0.9745   | 0.9754    | 0.9745 | 0.9746 |
| 0.1089        | 0.55  | 11000 | 0.0896          | 0.9688   | 0.9703    | 0.9688 | 0.9689 |
| 0.1125        | 0.575 | 11500 | 0.0632          | 0.9782   | 0.9787    | 0.9782 | 0.9782 |
| 0.11          | 0.6   | 12000 | 0.0775          | 0.9691   | 0.9708    | 0.9691 | 0.9692 |
| 0.1028        | 0.625 | 12500 | 0.0833          | 0.9698   | 0.9708    | 0.9698 | 0.9698 |
| 0.1052        | 0.65  | 13000 | 0.0663          | 0.9751   | 0.9755    | 0.9751 | 0.9751 |
| 0.1068        | 0.675 | 13500 | 0.0648          | 0.9772   | 0.9774    | 0.9772 | 0.9772 |
| 0.1029        | 0.7   | 14000 | 0.0962          | 0.9688   | 0.9706    | 0.9688 | 0.9689 |
| 0.1014        | 0.725 | 14500 | 0.0686          | 0.9772   | 0.9775    | 0.9772 | 0.9771 |
| 0.0978        | 0.75  | 15000 | 0.0802          | 0.9745   | 0.9752    | 0.9745 | 0.9745 |
| 0.095         | 0.775 | 15500 | 0.0646          | 0.9758   | 0.9763    | 0.9758 | 0.9758 |
| 0.0996        | 0.8   | 16000 | 0.0711          | 0.9758   | 0.9761    | 0.9758 | 0.9758 |
| 0.0967        | 0.825 | 16500 | 0.0683          | 0.9761   | 0.9768    | 0.9761 | 0.9761 |
| 0.0939        | 0.85  | 17000 | 0.0572          | 0.9792   | 0.9795    | 0.9792 | 0.9791 |
| 0.0966        | 0.875 | 17500 | 0.0527          | 0.9792   | 0.9794    | 0.9792 | 0.9791 |
| 0.0925        | 0.9   | 18000 | 0.0581          | 0.9798   | 0.9802    | 0.9798 | 0.9799 |
| 0.0945        | 0.925 | 18500 | 0.0693          | 0.9768   | 0.9776    | 0.9768 | 0.9768 |
| 0.0923        | 0.95  | 19000 | 0.0615          | 0.9785   | 0.9790    | 0.9785 | 0.9785 |
| 0.0896        | 0.975 | 19500 | 0.0643          | 0.9758   | 0.9766    | 0.9758 | 0.9758 |
| 0.0979        | 1.0   | 20000 | 0.0619          | 0.9765   | 0.9770    | 0.9765 | 0.9765 |


### Framework versions

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