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---
library_name: transformers
license: apache-2.0
base_model: yigagilbert/t5_efficient_small_language_ID
tags:
- generated_from_trainer
datasets:
- generator
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: t5_small_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: 0.658879605381663
name: Accuracy
- type: precision
value: 0.6928469419086497
name: Precision
- type: recall
value: 0.658879605381663
name: Recall
- type: f1
value: 0.6286369104782076
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. -->
# t5_small_language_Classification
This model is a fine-tuned version of [yigagilbert/t5_efficient_small_language_ID](https://huggingface.co/yigagilbert/t5_efficient_small_language_ID) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6482
- Accuracy: 0.6589
- Precision: 0.6928
- Recall: 0.6589
- F1: 0.6286
## 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: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 1000
- training_steps: 60000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.6453 | 0.0083 | 500 | 1.7792 | 0.5575 | 0.6272 | 0.5575 | 0.5283 |
| 0.3701 | 0.0167 | 1000 | 2.8566 | 0.4925 | 0.6309 | 0.4925 | 0.4427 |
| 0.3602 | 0.025 | 1500 | 3.4108 | 0.4331 | 0.6188 | 0.4331 | 0.3903 |
| 0.3573 | 0.0333 | 2000 | 1.9821 | 0.5855 | 0.6303 | 0.5855 | 0.5419 |
| 0.4229 | 0.0417 | 2500 | 1.9248 | 0.6071 | 0.6712 | 0.6071 | 0.5731 |
| 0.2156 | 0.05 | 3000 | 2.6673 | 0.5217 | 0.6906 | 0.5217 | 0.4851 |
| 0.3752 | 0.0583 | 3500 | 1.9381 | 0.5984 | 0.6682 | 0.5984 | 0.5619 |
| 0.4996 | 0.0667 | 4000 | 1.5622 | 0.6266 | 0.6757 | 0.6266 | 0.6022 |
| 0.2773 | 0.075 | 4500 | 1.8355 | 0.6299 | 0.6892 | 0.6299 | 0.5872 |
| 0.2815 | 0.0833 | 5000 | 1.7752 | 0.6423 | 0.6905 | 0.6423 | 0.6034 |
| 0.2525 | 0.0917 | 5500 | 1.6552 | 0.6450 | 0.6879 | 0.6450 | 0.6082 |
| 0.2271 | 0.1 | 6000 | 1.6523 | 0.6575 | 0.6916 | 0.6575 | 0.6278 |
| 0.3591 | 0.1083 | 6500 | 1.7169 | 0.6542 | 0.6985 | 0.6542 | 0.6238 |
| 0.2659 | 0.1167 | 7000 | 1.7209 | 0.6439 | 0.7090 | 0.6439 | 0.6180 |
| 0.2337 | 0.125 | 7500 | 1.7631 | 0.6531 | 0.7019 | 0.6531 | 0.6158 |
### Framework versions
- Transformers 4.57.1
- Pytorch 2.9.0+cu128
- Datasets 4.3.0
- Tokenizers 0.22.1
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