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--- |
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library_name: transformers |
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license: mit |
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base_model: microsoft/mdeberta-v3-base |
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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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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: mdeberta-domain_fold1 |
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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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# mdeberta-domain_fold1 |
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This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4601 |
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- Accuracy: 0.8630 |
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- Precision: 0.8456 |
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- Recall: 0.8212 |
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- F1: 0.8320 |
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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: 2e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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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: linear |
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- num_epochs: 10 |
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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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| 1.0441 | 1.0 | 19 | 0.8982 | 0.5890 | 0.8630 | 0.3333 | 0.2471 | |
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| 0.8108 | 2.0 | 38 | 0.7417 | 0.5890 | 0.8630 | 0.3333 | 0.2471 | |
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| 0.7043 | 3.0 | 57 | 0.7361 | 0.6438 | 0.7410 | 0.4222 | 0.3842 | |
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| 0.5828 | 4.0 | 76 | 0.6559 | 0.7192 | 0.6862 | 0.5517 | 0.5662 | |
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| 0.5089 | 5.0 | 95 | 0.5497 | 0.8562 | 0.8516 | 0.7811 | 0.7923 | |
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| 0.3767 | 6.0 | 114 | 0.5299 | 0.8425 | 0.8558 | 0.7517 | 0.7753 | |
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| 0.3405 | 7.0 | 133 | 0.4696 | 0.8699 | 0.8707 | 0.8034 | 0.8248 | |
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| 0.2441 | 8.0 | 152 | 0.4845 | 0.8425 | 0.8207 | 0.8168 | 0.8186 | |
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| 0.2385 | 9.0 | 171 | 0.4611 | 0.8767 | 0.8706 | 0.8217 | 0.8400 | |
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| 0.1887 | 10.0 | 190 | 0.4601 | 0.8630 | 0.8456 | 0.8212 | 0.8320 | |
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### Framework versions |
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- Transformers 4.46.0 |
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- Pytorch 2.3.1 |
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- Datasets 2.21.0 |
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- Tokenizers 0.20.1 |
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