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--- |
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license: mit |
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base_model: microsoft/deberta-v3-large |
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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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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: metacognitive-cls |
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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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# metacognitive-cls |
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This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1024 |
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- Accuracy: 0.9640 |
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- F1: 0.8326 |
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- Precision: 0.8742 |
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- Recall: 0.7947 |
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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: 9.946303722432942e-06 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 12 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.6685 | 1.0 | 76 | 0.6265 | 0.7931 | 0.0543 | 0.0559 | 0.0528 | |
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| 0.45 | 2.0 | 152 | 0.2973 | 0.8983 | 0.3275 | 0.6410 | 0.2199 | |
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| 0.2947 | 3.0 | 228 | 0.2671 | 0.9069 | 0.4910 | 0.6385 | 0.3988 | |
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| 0.2561 | 4.0 | 304 | 0.2246 | 0.9234 | 0.5323 | 0.8516 | 0.3871 | |
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| 0.2201 | 5.0 | 380 | 0.1926 | 0.9442 | 0.6988 | 0.8909 | 0.5748 | |
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| 0.1896 | 6.0 | 456 | 0.1704 | 0.9439 | 0.6828 | 0.9385 | 0.5367 | |
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| 0.1574 | 7.0 | 532 | 0.1468 | 0.9515 | 0.7452 | 0.9110 | 0.6305 | |
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| 0.1203 | 8.0 | 608 | 0.1213 | 0.9591 | 0.8056 | 0.8653 | 0.7537 | |
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| 0.0924 | 9.0 | 684 | 0.1119 | 0.9634 | 0.8290 | 0.8734 | 0.7889 | |
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| 0.0771 | 10.0 | 760 | 0.1073 | 0.9620 | 0.8206 | 0.8767 | 0.7713 | |
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| 0.067 | 11.0 | 836 | 0.1016 | 0.9657 | 0.8415 | 0.8762 | 0.8094 | |
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| 0.0609 | 12.0 | 912 | 0.1024 | 0.9640 | 0.8326 | 0.8742 | 0.7947 | |
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### Framework versions |
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- Transformers 4.40.1 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.0 |
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- Tokenizers 0.19.1 |
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