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--- |
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library_name: transformers |
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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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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: piiranha-1 |
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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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# piiranha-1 |
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This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0173 |
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- Precision: 0.9316 |
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- Recall: 0.9308 |
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- F1: 0.9312 |
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- Accuracy: 0.9944 |
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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: 5e-05 |
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- train_batch_size: 128 |
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- eval_batch_size: 128 |
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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_ratio: 0.05 |
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- num_epochs: 5 |
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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 | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.2984 | 0.0983 | 250 | 0.1005 | 0.5446 | 0.6111 | 0.5759 | 0.9702 | |
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| 0.0568 | 0.1965 | 500 | 0.0464 | 0.7895 | 0.8459 | 0.8167 | 0.9849 | |
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| 0.0441 | 0.2948 | 750 | 0.0400 | 0.8346 | 0.8669 | 0.8504 | 0.9869 | |
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| 0.0368 | 0.3931 | 1000 | 0.0320 | 0.8531 | 0.8784 | 0.8656 | 0.9891 | |
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| 0.0323 | 0.4914 | 1250 | 0.0293 | 0.8779 | 0.8889 | 0.8834 | 0.9903 | |
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| 0.0287 | 0.5896 | 1500 | 0.0269 | 0.8919 | 0.8836 | 0.8877 | 0.9907 | |
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| 0.0282 | 0.6879 | 1750 | 0.0276 | 0.8724 | 0.9012 | 0.8866 | 0.9903 | |
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| 0.0268 | 0.7862 | 2000 | 0.0254 | 0.8890 | 0.9041 | 0.8965 | 0.9914 | |
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| 0.0264 | 0.8844 | 2250 | 0.0236 | 0.8886 | 0.9040 | 0.8962 | 0.9915 | |
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| 0.0243 | 0.9827 | 2500 | 0.0232 | 0.8998 | 0.9033 | 0.9015 | 0.9917 | |
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| 0.0213 | 1.0810 | 2750 | 0.0237 | 0.9115 | 0.9040 | 0.9077 | 0.9923 | |
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| 0.0213 | 1.1792 | 3000 | 0.0222 | 0.9123 | 0.9143 | 0.9133 | 0.9925 | |
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| 0.0217 | 1.2775 | 3250 | 0.0222 | 0.8999 | 0.9169 | 0.9083 | 0.9924 | |
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| 0.0209 | 1.3758 | 3500 | 0.0212 | 0.9111 | 0.9133 | 0.9122 | 0.9928 | |
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| 0.0204 | 1.4741 | 3750 | 0.0206 | 0.9054 | 0.9203 | 0.9128 | 0.9926 | |
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| 0.0183 | 1.5723 | 4000 | 0.0212 | 0.9126 | 0.9160 | 0.9143 | 0.9927 | |
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| 0.0191 | 1.6706 | 4250 | 0.0192 | 0.9122 | 0.9192 | 0.9157 | 0.9929 | |
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| 0.0185 | 1.7689 | 4500 | 0.0195 | 0.9200 | 0.9191 | 0.9196 | 0.9932 | |
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| 0.018 | 1.8671 | 4750 | 0.0188 | 0.9136 | 0.9215 | 0.9176 | 0.9933 | |
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| 0.0183 | 1.9654 | 5000 | 0.0191 | 0.9179 | 0.9212 | 0.9196 | 0.9934 | |
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| 0.0147 | 2.0637 | 5250 | 0.0188 | 0.9246 | 0.9242 | 0.9244 | 0.9937 | |
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| 0.0149 | 2.1619 | 5500 | 0.0184 | 0.9188 | 0.9254 | 0.9221 | 0.9937 | |
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| 0.0143 | 2.2602 | 5750 | 0.0193 | 0.9187 | 0.9224 | 0.9205 | 0.9932 | |
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| 0.014 | 2.3585 | 6000 | 0.0190 | 0.9246 | 0.9280 | 0.9263 | 0.9936 | |
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| 0.0146 | 2.4568 | 6250 | 0.0190 | 0.9225 | 0.9277 | 0.9251 | 0.9936 | |
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| 0.0148 | 2.5550 | 6500 | 0.0175 | 0.9297 | 0.9306 | 0.9301 | 0.9942 | |
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| 0.0136 | 2.6533 | 6750 | 0.0172 | 0.9191 | 0.9329 | 0.9259 | 0.9938 | |
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| 0.0137 | 2.7516 | 7000 | 0.0166 | 0.9299 | 0.9312 | 0.9306 | 0.9942 | |
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| 0.014 | 2.8498 | 7250 | 0.0167 | 0.9285 | 0.9313 | 0.9299 | 0.9942 | |
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| 0.0128 | 2.9481 | 7500 | 0.0166 | 0.9271 | 0.9326 | 0.9298 | 0.9943 | |
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| 0.0113 | 3.0464 | 7750 | 0.0171 | 0.9286 | 0.9347 | 0.9316 | 0.9946 | |
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| 0.0103 | 3.1447 | 8000 | 0.0172 | 0.9284 | 0.9383 | 0.9334 | 0.9945 | |
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| 0.0104 | 3.2429 | 8250 | 0.0169 | 0.9312 | 0.9406 | 0.9359 | 0.9947 | |
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| 0.0094 | 3.3412 | 8500 | 0.0166 | 0.9368 | 0.9359 | 0.9364 | 0.9948 | |
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| 0.01 | 3.4395 | 8750 | 0.0166 | 0.9289 | 0.9387 | 0.9337 | 0.9944 | |
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| 0.0099 | 3.5377 | 9000 | 0.0162 | 0.9335 | 0.9332 | 0.9334 | 0.9947 | |
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| 0.0099 | 3.6360 | 9250 | 0.0160 | 0.9321 | 0.9380 | 0.9350 | 0.9947 | |
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| 0.01 | 3.7343 | 9500 | 0.0168 | 0.9306 | 0.9389 | 0.9347 | 0.9947 | |
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| 0.0101 | 3.8325 | 9750 | 0.0159 | 0.9339 | 0.9350 | 0.9344 | 0.9947 | |
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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 3.0.0 |
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- Tokenizers 0.19.1 |
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