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--- |
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library_name: transformers |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: eo_train1-10_eval11 |
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results: [] |
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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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# eo_train1-10_eval11 |
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This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6932 |
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- Accuracy: 0.5 |
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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: 0.001 |
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- train_batch_size: 128 |
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- eval_batch_size: 128 |
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- seed: 7658372 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH 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 |
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- lr_scheduler_warmup_ratio: 0.1 |
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- training_steps: 3000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:------:|:----:|:---------------:|:--------:| |
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| No log | 0 | 0 | 2.6863 | 0.0 | |
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| 0.6284 | 100.0 | 100 | 0.7859 | 0.5 | |
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| 0.548 | 200.0 | 200 | 0.7353 | 0.5 | |
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| 0.5022 | 300.0 | 300 | 0.6964 | 0.5 | |
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| 0.6935 | 400.0 | 400 | 0.6936 | 0.5 | |
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| 0.6933 | 500.0 | 500 | 0.6933 | 0.5 | |
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| 0.6932 | 600.0 | 600 | 0.6932 | 0.5 | |
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| 0.6932 | 700.0 | 700 | 0.6932 | 0.5 | |
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| 0.6932 | 800.0 | 800 | 0.6932 | 0.5 | |
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| 0.6932 | 900.0 | 900 | 0.6932 | 0.5 | |
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| 0.6932 | 1000.0 | 1000 | 0.6932 | 0.5 | |
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| 0.6932 | 1100.0 | 1100 | 0.6932 | 0.5 | |
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| 0.6932 | 1200.0 | 1200 | 0.6932 | 0.5 | |
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| 0.6932 | 1300.0 | 1300 | 0.6932 | 0.5 | |
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| 0.6932 | 1400.0 | 1400 | 0.6932 | 0.5 | |
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| 0.6932 | 1500.0 | 1500 | 0.6932 | 0.5 | |
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| 0.6932 | 1600.0 | 1600 | 0.6932 | 0.5 | |
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| 0.6932 | 1700.0 | 1700 | 0.6932 | 0.5 | |
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| 0.6932 | 1800.0 | 1800 | 0.6932 | 0.5 | |
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| 0.6932 | 1900.0 | 1900 | 0.6932 | 0.5 | |
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| 0.6932 | 2000.0 | 2000 | 0.6932 | 0.5 | |
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| 0.6932 | 2100.0 | 2100 | 0.6932 | 0.5 | |
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| 0.6932 | 2200.0 | 2200 | 0.6932 | 0.5 | |
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| 0.6932 | 2300.0 | 2300 | 0.6932 | 0.5 | |
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| 0.6932 | 2400.0 | 2400 | 0.6932 | 0.5 | |
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| 0.6932 | 2500.0 | 2500 | 0.6932 | 0.5 | |
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| 0.6932 | 2600.0 | 2600 | 0.6932 | 0.5 | |
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| 0.6932 | 2700.0 | 2700 | 0.6932 | 0.5 | |
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| 0.6932 | 2800.0 | 2800 | 0.6932 | 0.5 | |
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| 0.6932 | 2900.0 | 2900 | 0.6932 | 0.5 | |
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| 0.6932 | 3000.0 | 3000 | 0.6932 | 0.5 | |
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### Framework versions |
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- Transformers 4.46.0 |
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- Pytorch 2.5.1 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.1 |
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