README.md

metadata

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

t5_small_language_Classification

This model is a fine-tuned version of 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