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--- |
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library_name: transformers |
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base_model: microsoft/mpnet-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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model-index: |
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- name: mpnet_token_cls_model |
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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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# mpnet_token_cls_model |
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This model is a fine-tuned version of [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1318 |
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- F1: 0.8327 |
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- Precision: 0.8373 |
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- Recall: 0.8281 |
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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: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 4 |
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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 | F1 | Precision | Recall | |
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|:-------------:|:------:|:-----:|:---------------:|:------:|:---------:|:------:| |
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| 0.174 | 0.1553 | 1000 | 0.1766 | 0.7930 | 0.8137 | 0.7734 | |
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| 0.1408 | 0.3105 | 2000 | 0.1469 | 0.8030 | 0.8143 | 0.7920 | |
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| 0.1326 | 0.4658 | 3000 | 0.1313 | 0.8187 | 0.8399 | 0.7985 | |
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| 0.1283 | 0.6210 | 4000 | 0.1308 | 0.8169 | 0.8188 | 0.8150 | |
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| 0.1259 | 0.7763 | 5000 | 0.1270 | 0.8195 | 0.8325 | 0.8069 | |
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| 0.12 | 0.9315 | 6000 | 0.1224 | 0.8162 | 0.8272 | 0.8055 | |
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| 0.1072 | 1.0868 | 7000 | 0.1221 | 0.8215 | 0.82 | 0.8230 | |
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| 0.1068 | 1.2420 | 8000 | 0.1216 | 0.8208 | 0.8234 | 0.8182 | |
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| 0.1022 | 1.3973 | 9000 | 0.1256 | 0.8234 | 0.8188 | 0.8281 | |
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| 0.1034 | 1.5526 | 10000 | 0.1217 | 0.8267 | 0.8292 | 0.8241 | |
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| 0.1051 | 1.7078 | 11000 | 0.1203 | 0.8288 | 0.8435 | 0.8146 | |
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| 0.1011 | 1.8631 | 12000 | 0.1246 | 0.8299 | 0.8284 | 0.8314 | |
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| 0.0917 | 2.0183 | 13000 | 0.1266 | 0.8248 | 0.8274 | 0.8223 | |
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| 0.0887 | 2.1736 | 14000 | 0.1213 | 0.8261 | 0.8260 | 0.8263 | |
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| 0.0863 | 2.3288 | 15000 | 0.1255 | 0.8272 | 0.8263 | 0.8281 | |
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| 0.0897 | 2.4841 | 16000 | 0.1265 | 0.8210 | 0.8302 | 0.8120 | |
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| 0.0835 | 2.6393 | 17000 | 0.1233 | 0.8299 | 0.8284 | 0.8314 | |
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| 0.0833 | 2.7946 | 18000 | 0.1259 | 0.8341 | 0.8398 | 0.8285 | |
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| 0.0829 | 2.9499 | 19000 | 0.1189 | 0.8328 | 0.8397 | 0.8259 | |
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| 0.0704 | 3.1051 | 20000 | 0.1308 | 0.8302 | 0.8290 | 0.8314 | |
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| 0.073 | 3.2604 | 21000 | 0.1273 | 0.8296 | 0.8330 | 0.8263 | |
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| 0.0711 | 3.4156 | 22000 | 0.1335 | 0.8304 | 0.8399 | 0.8212 | |
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| 0.0695 | 3.5709 | 23000 | 0.1325 | 0.8283 | 0.8353 | 0.8215 | |
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| 0.0708 | 3.7261 | 24000 | 0.1316 | 0.8319 | 0.8384 | 0.8255 | |
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| 0.0706 | 3.8814 | 25000 | 0.1318 | 0.8327 | 0.8373 | 0.8281 | |
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### Framework versions |
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- Transformers 4.50.0 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.21.4 |
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