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library_name: transformers
base_model: microsoft/mpnet-base
tags:
- generated_from_trainer
metrics:
- f1
- precision
- recall
model-index:
- name: mpnet_token_cls_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mpnet_token_cls_model
This model is a fine-tuned version of [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1318
- F1: 0.8327
- Precision: 0.8373
- Recall: 0.8281
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall |
|:-------------:|:------:|:-----:|:---------------:|:------:|:---------:|:------:|
| 0.174 | 0.1553 | 1000 | 0.1766 | 0.7930 | 0.8137 | 0.7734 |
| 0.1408 | 0.3105 | 2000 | 0.1469 | 0.8030 | 0.8143 | 0.7920 |
| 0.1326 | 0.4658 | 3000 | 0.1313 | 0.8187 | 0.8399 | 0.7985 |
| 0.1283 | 0.6210 | 4000 | 0.1308 | 0.8169 | 0.8188 | 0.8150 |
| 0.1259 | 0.7763 | 5000 | 0.1270 | 0.8195 | 0.8325 | 0.8069 |
| 0.12 | 0.9315 | 6000 | 0.1224 | 0.8162 | 0.8272 | 0.8055 |
| 0.1072 | 1.0868 | 7000 | 0.1221 | 0.8215 | 0.82 | 0.8230 |
| 0.1068 | 1.2420 | 8000 | 0.1216 | 0.8208 | 0.8234 | 0.8182 |
| 0.1022 | 1.3973 | 9000 | 0.1256 | 0.8234 | 0.8188 | 0.8281 |
| 0.1034 | 1.5526 | 10000 | 0.1217 | 0.8267 | 0.8292 | 0.8241 |
| 0.1051 | 1.7078 | 11000 | 0.1203 | 0.8288 | 0.8435 | 0.8146 |
| 0.1011 | 1.8631 | 12000 | 0.1246 | 0.8299 | 0.8284 | 0.8314 |
| 0.0917 | 2.0183 | 13000 | 0.1266 | 0.8248 | 0.8274 | 0.8223 |
| 0.0887 | 2.1736 | 14000 | 0.1213 | 0.8261 | 0.8260 | 0.8263 |
| 0.0863 | 2.3288 | 15000 | 0.1255 | 0.8272 | 0.8263 | 0.8281 |
| 0.0897 | 2.4841 | 16000 | 0.1265 | 0.8210 | 0.8302 | 0.8120 |
| 0.0835 | 2.6393 | 17000 | 0.1233 | 0.8299 | 0.8284 | 0.8314 |
| 0.0833 | 2.7946 | 18000 | 0.1259 | 0.8341 | 0.8398 | 0.8285 |
| 0.0829 | 2.9499 | 19000 | 0.1189 | 0.8328 | 0.8397 | 0.8259 |
| 0.0704 | 3.1051 | 20000 | 0.1308 | 0.8302 | 0.8290 | 0.8314 |
| 0.073 | 3.2604 | 21000 | 0.1273 | 0.8296 | 0.8330 | 0.8263 |
| 0.0711 | 3.4156 | 22000 | 0.1335 | 0.8304 | 0.8399 | 0.8212 |
| 0.0695 | 3.5709 | 23000 | 0.1325 | 0.8283 | 0.8353 | 0.8215 |
| 0.0708 | 3.7261 | 24000 | 0.1316 | 0.8319 | 0.8384 | 0.8255 |
| 0.0706 | 3.8814 | 25000 | 0.1318 | 0.8327 | 0.8373 | 0.8281 |
### Framework versions
- Transformers 4.50.0
- Pytorch 2.5.1+cu121
- Datasets 2.21.0
- Tokenizers 0.21.4
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