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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: yigagilbert/t5_efficient_small_language_ID |
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tags: |
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- generated_from_trainer |
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datasets: |
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- generator |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: t5_small_language_Classification |
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results: |
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- task: |
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type: text-classification |
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name: Text Classification |
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dataset: |
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name: generator |
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type: generator |
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config: default |
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split: train |
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args: default |
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metrics: |
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- type: accuracy |
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value: 0.658879605381663 |
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name: Accuracy |
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- type: precision |
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value: 0.6928469419086497 |
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name: Precision |
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- type: recall |
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value: 0.658879605381663 |
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name: Recall |
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- type: f1 |
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value: 0.6286369104782076 |
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name: F1 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# t5_small_language_Classification |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.6482 |
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- Accuracy: 0.6589 |
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- Precision: 0.6928 |
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- Recall: 0.6589 |
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- F1: 0.6286 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0005 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 128 |
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- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: cosine_with_restarts |
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- lr_scheduler_warmup_steps: 1000 |
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- training_steps: 60000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 0.6453 | 0.0083 | 500 | 1.7792 | 0.5575 | 0.6272 | 0.5575 | 0.5283 | |
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| 0.3701 | 0.0167 | 1000 | 2.8566 | 0.4925 | 0.6309 | 0.4925 | 0.4427 | |
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| 0.3602 | 0.025 | 1500 | 3.4108 | 0.4331 | 0.6188 | 0.4331 | 0.3903 | |
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| 0.3573 | 0.0333 | 2000 | 1.9821 | 0.5855 | 0.6303 | 0.5855 | 0.5419 | |
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| 0.4229 | 0.0417 | 2500 | 1.9248 | 0.6071 | 0.6712 | 0.6071 | 0.5731 | |
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| 0.2156 | 0.05 | 3000 | 2.6673 | 0.5217 | 0.6906 | 0.5217 | 0.4851 | |
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| 0.3752 | 0.0583 | 3500 | 1.9381 | 0.5984 | 0.6682 | 0.5984 | 0.5619 | |
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| 0.4996 | 0.0667 | 4000 | 1.5622 | 0.6266 | 0.6757 | 0.6266 | 0.6022 | |
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| 0.2773 | 0.075 | 4500 | 1.8355 | 0.6299 | 0.6892 | 0.6299 | 0.5872 | |
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| 0.2815 | 0.0833 | 5000 | 1.7752 | 0.6423 | 0.6905 | 0.6423 | 0.6034 | |
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| 0.2525 | 0.0917 | 5500 | 1.6552 | 0.6450 | 0.6879 | 0.6450 | 0.6082 | |
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| 0.2271 | 0.1 | 6000 | 1.6523 | 0.6575 | 0.6916 | 0.6575 | 0.6278 | |
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| 0.3591 | 0.1083 | 6500 | 1.7169 | 0.6542 | 0.6985 | 0.6542 | 0.6238 | |
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| 0.2659 | 0.1167 | 7000 | 1.7209 | 0.6439 | 0.7090 | 0.6439 | 0.6180 | |
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| 0.2337 | 0.125 | 7500 | 1.7631 | 0.6531 | 0.7019 | 0.6531 | 0.6158 | |
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### Framework versions |
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- Transformers 4.57.1 |
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- Pytorch 2.9.0+cu128 |
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- Datasets 4.3.0 |
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- Tokenizers 0.22.1 |
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