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--- |
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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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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: scenario_4 |
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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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# scenario_4 |
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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: 0.1764 |
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- Accuracy: 0.9704 |
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- F1: 0.9704 |
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- Precision: 0.9710 |
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- Recall: 0.9704 |
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- Accuracy Label Test: 0.9879 |
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- Accuracy Label Train: 0.9536 |
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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: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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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: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Accuracy Label Test | Accuracy Label Train | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------------:|:--------------------:| |
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| 0.5515 | 0.1579 | 100 | 0.5194 | 0.7579 | 0.7415 | 0.8352 | 0.7579 | 0.5070 | 0.9992 | |
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| 0.2205 | 0.3157 | 200 | 0.2537 | 0.9300 | 0.9298 | 0.9361 | 0.9300 | 0.9883 | 0.8739 | |
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| 0.1106 | 0.4736 | 300 | 0.3450 | 0.9129 | 0.9124 | 0.9248 | 0.9129 | 0.9960 | 0.8329 | |
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| 0.0384 | 0.6314 | 400 | 0.1408 | 0.9683 | 0.9683 | 0.9687 | 0.9683 | 0.9835 | 0.9536 | |
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| 0.0631 | 0.7893 | 500 | 0.1517 | 0.9631 | 0.9631 | 0.9645 | 0.9631 | 0.9895 | 0.9377 | |
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| 0.0276 | 0.9471 | 600 | 0.3649 | 0.9387 | 0.9386 | 0.9444 | 0.9387 | 0.9948 | 0.8847 | |
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| 0.0245 | 1.1050 | 700 | 0.1339 | 0.9702 | 0.9702 | 0.9702 | 0.9702 | 0.9727 | 0.9679 | |
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| 0.0519 | 1.2628 | 800 | 0.4945 | 0.9186 | 0.9182 | 0.9299 | 0.9186 | 0.9992 | 0.8410 | |
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| 0.02 | 1.4207 | 900 | 0.2637 | 0.9549 | 0.9548 | 0.9580 | 0.9549 | 0.9960 | 0.9153 | |
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| 0.0325 | 1.5785 | 1000 | 0.1165 | 0.9708 | 0.9708 | 0.9712 | 0.9708 | 0.9851 | 0.9571 | |
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| 0.016 | 1.7364 | 1100 | 0.1007 | 0.9692 | 0.9692 | 0.9697 | 0.9692 | 0.9530 | 0.9849 | |
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| 0.0068 | 1.8942 | 1200 | 0.1679 | 0.9690 | 0.9690 | 0.9697 | 0.9690 | 0.9871 | 0.9516 | |
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| 0.0042 | 2.0521 | 1300 | 0.1182 | 0.9734 | 0.9734 | 0.9734 | 0.9734 | 0.9723 | 0.9745 | |
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| 0.0005 | 2.2099 | 1400 | 0.1432 | 0.9730 | 0.9730 | 0.9731 | 0.9730 | 0.9799 | 0.9663 | |
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| 0.0182 | 2.3678 | 1500 | 0.1460 | 0.9718 | 0.9718 | 0.9723 | 0.9718 | 0.9871 | 0.9571 | |
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| 0.0004 | 2.5257 | 1600 | 0.1383 | 0.9732 | 0.9732 | 0.9734 | 0.9732 | 0.9843 | 0.9625 | |
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| 0.0003 | 2.6835 | 1700 | 0.1381 | 0.9744 | 0.9744 | 0.9745 | 0.9744 | 0.9831 | 0.9660 | |
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| 0.0002 | 2.8414 | 1800 | 0.1599 | 0.9724 | 0.9724 | 0.9728 | 0.9724 | 0.9863 | 0.9590 | |
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### Framework versions |
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- Transformers 4.44.0 |
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- Pytorch 2.3.1 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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