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--- |
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license: mit |
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base_model: microsoft/deberta-v3-base |
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tags: |
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- multi-label text classification |
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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: deberta_classifier |
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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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# deberta_classifier |
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This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0183 |
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- Accuracy: 0.9955 |
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- F1: 0.6062 |
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- Precision: 0.8225 |
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- Recall: 0.4799 |
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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: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 16 |
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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: 2 |
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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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| 0.6159 | 0.1169 | 100 | 0.5955 | 0.7621 | 0.0288 | 0.0148 | 0.4839 | |
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| 0.3536 | 0.2338 | 200 | 0.3085 | 0.9753 | 0.1645 | 0.1091 | 0.3341 | |
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| 0.1166 | 0.3507 | 300 | 0.0917 | 0.9931 | 0.4124 | 0.5429 | 0.3325 | |
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| 0.0456 | 0.4676 | 400 | 0.0375 | 0.9931 | 0.4124 | 0.5429 | 0.3325 | |
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| 0.0308 | 0.5845 | 500 | 0.0270 | 0.9931 | 0.4124 | 0.5429 | 0.3325 | |
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| 0.0249 | 0.7013 | 600 | 0.0234 | 0.9942 | 0.4459 | 0.7407 | 0.3189 | |
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| 0.0231 | 0.8182 | 700 | 0.0211 | 0.9953 | 0.5983 | 0.7970 | 0.4789 | |
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| 0.0213 | 0.9351 | 800 | 0.0196 | 0.9953 | 0.5989 | 0.7998 | 0.4787 | |
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| 0.0197 | 1.0520 | 900 | 0.0187 | 0.9954 | 0.6029 | 0.8168 | 0.4778 | |
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| 0.0205 | 1.1689 | 1000 | 0.0183 | 0.9955 | 0.6062 | 0.8225 | 0.4799 | |
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| 0.017 | 1.2858 | 1100 | 0.0175 | 0.9959 | 0.6610 | 0.8426 | 0.5437 | |
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| 0.018 | 1.4027 | 1200 | 0.0170 | 0.9960 | 0.6653 | 0.8685 | 0.5392 | |
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| 0.0177 | 1.5196 | 1300 | 0.0165 | 0.9961 | 0.6722 | 0.8732 | 0.5464 | |
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| 0.0189 | 1.6365 | 1400 | 0.0162 | 0.9962 | 0.6752 | 0.8910 | 0.5435 | |
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| 0.0179 | 1.7534 | 1500 | 0.0159 | 0.9964 | 0.6898 | 0.9151 | 0.5535 | |
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| 0.0169 | 1.8703 | 1600 | 0.0158 | 0.9964 | 0.6928 | 0.9030 | 0.5620 | |
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| 0.0172 | 1.9871 | 1700 | 0.0156 | 0.9964 | 0.6909 | 0.9130 | 0.5557 | |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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