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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: google/t5-efficient-small |
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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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model-index: |
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- name: t5_efficient_small_language_ID |
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results: |
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- task: |
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type: text2text-generation |
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name: Sequence-to-sequence Language Modeling |
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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: 0.6577572709259952 |
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name: Accuracy |
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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_efficient_small_language_ID |
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This model is a fine-tuned version of [google/t5-efficient-small](https://huggingface.co/google/t5-efficient-small) on the generator dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4285 |
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- Accuracy: 0.6578 |
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- F1 Macro: 0.5633 |
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- F1 Weighted: 0.6050 |
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- Precision Macro: 0.6452 |
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- Recall Macro: 0.6124 |
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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 | F1 Macro | F1 Weighted | Precision Macro | Recall Macro | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|:-----------:|:---------------:|:------------:| |
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| 0.3649 | 0.0083 | 500 | 0.8746 | 0.2458 | 0.1941 | 0.2013 | 0.2757 | 0.2370 | |
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| 0.1204 | 0.0167 | 1000 | 0.8914 | 0.3442 | 0.2543 | 0.2637 | 0.4155 | 0.3319 | |
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| 0.0788 | 0.025 | 1500 | 1.0181 | 0.3853 | 0.3001 | 0.3001 | 0.4832 | 0.3853 | |
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| 0.0771 | 0.0333 | 2000 | 0.5361 | 0.5775 | 0.4982 | 0.5166 | 0.5265 | 0.5569 | |
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| 0.0737 | 0.0417 | 2500 | 0.6765 | 0.5442 | 0.4678 | 0.4851 | 0.5409 | 0.5248 | |
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| 0.0399 | 0.05 | 3000 | 0.6103 | 0.5444 | 0.4692 | 0.4866 | 0.5858 | 0.5250 | |
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| 0.0557 | 0.0583 | 3500 | 0.4436 | 0.6128 | 0.5453 | 0.5655 | 0.6635 | 0.5909 | |
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| 0.0963 | 0.0667 | 4000 | 0.4755 | 0.6027 | 0.5328 | 0.5526 | 0.6001 | 0.5812 | |
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| 0.0282 | 0.075 | 4500 | 0.4607 | 0.6347 | 0.5728 | 0.5728 | 0.6121 | 0.6347 | |
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| 0.0386 | 0.0833 | 5000 | 0.5344 | 0.6501 | 0.5574 | 0.5781 | 0.6186 | 0.6269 | |
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| 0.0355 | 0.0917 | 5500 | 0.4191 | 0.6575 | 0.5793 | 0.6008 | 0.6199 | 0.6340 | |
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| 0.0244 | 0.1 | 6000 | 0.4040 | 0.6802 | 0.5880 | 0.6316 | 0.6406 | 0.6333 | |
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| 0.0331 | 0.1083 | 6500 | 0.4438 | 0.6517 | 0.6053 | 0.6053 | 0.7090 | 0.6517 | |
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| 0.0224 | 0.1167 | 7000 | 0.4869 | 0.6649 | 0.5878 | 0.6096 | 0.6689 | 0.6412 | |
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| 0.0263 | 0.125 | 7500 | 0.4285 | 0.6578 | 0.5633 | 0.6050 | 0.6452 | 0.6124 | |
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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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