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---
license: apache-2.0
tags:
- generated_from_trainer
base_model: google/t5-efficient-tiny
datasets:
- generator
metrics:
- accuracy
- precision
- recall
- f1
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
---
<!-- 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: 0.0615
- 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
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