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--- |
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library_name: transformers |
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license: mit |
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base_model: microsoft/deberta-v3-small |
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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: doc-topic-model_eval-04_train-00 |
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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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# doc-topic-model_eval-04_train-00 |
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This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0377 |
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- Accuracy: 0.9880 |
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- F1: 0.6385 |
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- Precision: 0.7205 |
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- Recall: 0.5733 |
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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: 4 |
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- eval_batch_size: 256 |
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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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- num_epochs: 100 |
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- mixed_precision_training: Native AMP |
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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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| 0.0929 | 0.4931 | 1000 | 0.0906 | 0.9815 | 0.0 | 0.0 | 0.0 | |
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| 0.0785 | 0.9862 | 2000 | 0.0704 | 0.9815 | 0.0 | 0.0 | 0.0 | |
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| 0.0622 | 1.4793 | 3000 | 0.0572 | 0.9823 | 0.1022 | 0.8442 | 0.0544 | |
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| 0.0542 | 1.9724 | 4000 | 0.0499 | 0.9843 | 0.3390 | 0.7662 | 0.2176 | |
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| 0.048 | 2.4655 | 5000 | 0.0459 | 0.9853 | 0.4278 | 0.7709 | 0.2960 | |
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| 0.0436 | 2.9586 | 6000 | 0.0429 | 0.9863 | 0.5135 | 0.7466 | 0.3913 | |
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| 0.0384 | 3.4517 | 7000 | 0.0411 | 0.9868 | 0.5512 | 0.7418 | 0.4386 | |
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| 0.0385 | 3.9448 | 8000 | 0.0396 | 0.9868 | 0.5391 | 0.7659 | 0.4159 | |
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| 0.0343 | 4.4379 | 9000 | 0.0392 | 0.9870 | 0.5622 | 0.7475 | 0.4505 | |
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| 0.0343 | 4.9310 | 10000 | 0.0383 | 0.9872 | 0.5747 | 0.7490 | 0.4662 | |
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| 0.0304 | 5.4241 | 11000 | 0.0381 | 0.9873 | 0.5883 | 0.7375 | 0.4894 | |
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| 0.0299 | 5.9172 | 12000 | 0.0367 | 0.9877 | 0.6116 | 0.7341 | 0.5242 | |
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| 0.0265 | 6.4103 | 13000 | 0.0374 | 0.9876 | 0.6157 | 0.7219 | 0.5367 | |
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| 0.0261 | 6.9034 | 14000 | 0.0365 | 0.9879 | 0.6179 | 0.7448 | 0.5279 | |
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| 0.0236 | 7.3964 | 15000 | 0.0374 | 0.9877 | 0.6228 | 0.7218 | 0.5476 | |
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| 0.0236 | 7.8895 | 16000 | 0.0372 | 0.9880 | 0.6263 | 0.7356 | 0.5453 | |
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| 0.0215 | 8.3826 | 17000 | 0.0376 | 0.9879 | 0.6326 | 0.7199 | 0.5642 | |
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| 0.0216 | 8.8757 | 18000 | 0.0381 | 0.9878 | 0.6322 | 0.7149 | 0.5666 | |
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| 0.0177 | 9.3688 | 19000 | 0.0377 | 0.9880 | 0.6385 | 0.7205 | 0.5733 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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