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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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- generated_from_trainer |
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metrics: |
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- f1 |
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- precision |
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- recall |
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- accuracy |
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model-index: |
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- name: debert-imeocap |
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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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# debert-imeocap |
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This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.8660 |
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- F1: 0.6185 |
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- Precision: 0.6337 |
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- Recall: 0.6154 |
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- Accuracy: 0.6154 |
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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: 64 |
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- eval_batch_size: 64 |
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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: 15 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|:--------:| |
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| 0.4637 | 1.0 | 74 | 1.3864 | 0.6129 | 0.6262 | 0.6115 | 0.6115 | |
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| 0.3815 | 2.0 | 148 | 1.3801 | 0.6193 | 0.6348 | 0.6173 | 0.6173 | |
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| 0.3363 | 3.0 | 222 | 1.6944 | 0.6077 | 0.6297 | 0.6077 | 0.6077 | |
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| 0.31 | 4.0 | 296 | 1.6945 | 0.5995 | 0.6285 | 0.5942 | 0.5942 | |
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| 0.2885 | 5.0 | 370 | 1.5945 | 0.6218 | 0.6306 | 0.6192 | 0.6192 | |
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| 0.2594 | 6.0 | 444 | 1.7662 | 0.6279 | 0.6396 | 0.625 | 0.625 | |
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| 0.2319 | 7.0 | 518 | 1.7093 | 0.6210 | 0.6321 | 0.6173 | 0.6173 | |
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| 0.2306 | 8.0 | 592 | 1.8068 | 0.6279 | 0.6341 | 0.6288 | 0.6288 | |
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| 0.2167 | 9.0 | 666 | 1.7306 | 0.6376 | 0.6444 | 0.6346 | 0.6346 | |
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| 0.2158 | 10.0 | 740 | 1.8745 | 0.6262 | 0.6318 | 0.6269 | 0.6269 | |
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| 0.222 | 11.0 | 814 | 1.8323 | 0.6200 | 0.6348 | 0.6173 | 0.6173 | |
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| 0.2152 | 12.0 | 888 | 1.8576 | 0.6246 | 0.6363 | 0.6212 | 0.6212 | |
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| 0.226 | 13.0 | 962 | 1.8880 | 0.6343 | 0.6411 | 0.6308 | 0.6308 | |
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| 0.2097 | 14.0 | 1036 | 1.8884 | 0.6152 | 0.6326 | 0.6115 | 0.6115 | |
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| 0.2192 | 15.0 | 1110 | 1.8660 | 0.6185 | 0.6337 | 0.6154 | 0.6154 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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