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---
library_name: transformers
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: my-polarization-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. -->
# my-polarization-model
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1047
- Accuracy: 0.6357
- F1: 0.4941
- Precision: 0.4041
- Recall: 0.6357
## 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: 2e-07
- train_batch_size: 100
- eval_batch_size: 100
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Accuracy | F1 | Validation Loss | Precision | Recall |
|:-------------:|:--------:|:----:|:--------:|:------:|:---------------:|:---------:|:------:|
| 1.2002 | 3.8462 | 100 | 0.3643 | 0.1946 | 1.1760 | 0.1327 | 0.3643 |
| 1.1958 | 7.6923 | 200 | 0.3643 | 0.1946 | 1.1699 | 0.1327 | 0.3643 |
| 1.1865 | 11.5385 | 300 | 0.3643 | 0.1946 | 1.1643 | 0.1327 | 0.3643 |
| 1.1827 | 15.3846 | 400 | 0.3643 | 0.1946 | 1.1590 | 0.1327 | 0.3643 |
| 1.1763 | 19.2308 | 500 | 0.3643 | 0.1946 | 1.1541 | 0.1327 | 0.3643 |
| 1.1697 | 23.0769 | 600 | 0.3814 | 0.2352 | 1.1496 | 0.6857 | 0.3814 |
| 1.1686 | 26.9231 | 700 | 0.5566 | 0.5531 | 1.1454 | 0.6590 | 0.5566 |
| 1.1653 | 30.7692 | 800 | 0.6496 | 0.5773 | 1.1415 | 0.6270 | 0.6496 |
| 1.1614 | 34.6154 | 900 | 0.6372 | 0.5031 | 1.1379 | 0.6238 | 0.6372 |
| 1.1589 | 38.4615 | 1000 | 0.6341 | 0.4933 | 1.1347 | 0.4037 | 0.6341 |
| 1.1524 | 42.3077 | 1100 | 0.6357 | 0.4941 | 1.1316 | 0.4041 | 0.6357 |
| 1.1472 | 46.1538 | 1200 | 0.6357 | 0.4941 | 1.1288 | 0.4041 | 0.6357 |
| 1.1465 | 50.0 | 1300 | 0.6357 | 0.4941 | 1.1263 | 0.4041 | 0.6357 |
| 1.1479 | 53.8462 | 1400 | 0.6357 | 0.4941 | 1.1240 | 0.4041 | 0.6357 |
| 1.147 | 57.6923 | 1500 | 0.6357 | 0.4941 | 1.1219 | 0.4041 | 0.6357 |
| 1.1489 | 61.5385 | 1600 | 0.6357 | 0.4941 | 1.1201 | 0.4041 | 0.6357 |
| 1.1421 | 65.3846 | 1700 | 0.6357 | 0.4941 | 1.1184 | 0.4041 | 0.6357 |
| 1.1424 | 69.2308 | 1800 | 0.6357 | 0.4941 | 1.1169 | 0.4041 | 0.6357 |
| 1.1363 | 73.0769 | 1900 | 0.6357 | 0.4941 | 1.1156 | 0.4041 | 0.6357 |
| 1.14 | 76.9231 | 2000 | 0.6357 | 0.4941 | 1.1144 | 0.4041 | 0.6357 |
| 1.1399 | 80.7692 | 2100 | 0.6357 | 0.4941 | 1.1134 | 0.4041 | 0.6357 |
| 1.1403 | 84.6154 | 2200 | 0.6357 | 0.4941 | 1.1124 | 0.4041 | 0.6357 |
| 1.1417 | 88.4615 | 2300 | 0.6357 | 0.4941 | 1.1115 | 0.4041 | 0.6357 |
| 1.1352 | 92.3077 | 2400 | 0.6357 | 0.4941 | 1.1108 | 0.4041 | 0.6357 |
| 1.127 | 96.1538 | 2500 | 0.6357 | 0.4941 | 1.1101 | 0.4041 | 0.6357 |
| 1.1245 | 100.0 | 2600 | 0.6357 | 0.4941 | 1.1095 | 0.4041 | 0.6357 |
| 1.1309 | 103.8462 | 2700 | 0.6357 | 0.4941 | 1.1090 | 0.4041 | 0.6357 |
| 1.1318 | 107.6923 | 2800 | 0.6357 | 0.4941 | 1.1085 | 0.4041 | 0.6357 |
| 1.1293 | 111.5385 | 2900 | 0.6357 | 0.4941 | 1.1080 | 0.4041 | 0.6357 |
| 1.1315 | 115.3846 | 3000 | 0.6357 | 0.4941 | 1.1076 | 0.4041 | 0.6357 |
| 1.1299 | 119.2308 | 3100 | 0.6357 | 0.4941 | 1.1073 | 0.4041 | 0.6357 |
| 1.1314 | 123.0769 | 3200 | 0.6357 | 0.4941 | 1.1070 | 0.4041 | 0.6357 |
| 1.1309 | 126.9231 | 3300 | 0.6357 | 0.4941 | 1.1067 | 0.4041 | 0.6357 |
| 1.1235 | 130.7692 | 3400 | 0.6357 | 0.4941 | 1.1064 | 0.4041 | 0.6357 |
| 1.1367 | 134.6154 | 3500 | 0.6357 | 0.4941 | 1.1062 | 0.4041 | 0.6357 |
| 1.1362 | 138.4615 | 3600 | 0.6357 | 0.4941 | 1.1060 | 0.4041 | 0.6357 |
| 1.1194 | 142.3077 | 3700 | 0.6357 | 0.4941 | 1.1058 | 0.4041 | 0.6357 |
| 1.1283 | 146.1538 | 3800 | 0.6357 | 0.4941 | 1.1057 | 0.4041 | 0.6357 |
| 1.1183 | 150.0 | 3900 | 0.6357 | 0.4941 | 1.1055 | 0.4041 | 0.6357 |
| 1.1252 | 153.8462 | 4000 | 0.6357 | 0.4941 | 1.1054 | 0.4041 | 0.6357 |
| 1.1357 | 157.6923 | 4100 | 0.6357 | 0.4941 | 1.1053 | 0.4041 | 0.6357 |
| 1.132 | 161.5385 | 4200 | 0.6357 | 0.4941 | 1.1052 | 0.4041 | 0.6357 |
| 1.1292 | 165.3846 | 4300 | 0.6357 | 0.4941 | 1.1051 | 0.4041 | 0.6357 |
| 1.1302 | 169.2308 | 4400 | 0.6357 | 0.4941 | 1.1050 | 0.4041 | 0.6357 |
| 1.1282 | 173.0769 | 4500 | 0.6357 | 0.4941 | 1.1049 | 0.4041 | 0.6357 |
| 1.1323 | 176.9231 | 4600 | 1.1049 | 0.6357 | 0.4941 | 0.4041 | 0.6357 |
| 1.1368 | 180.7692 | 4700 | 1.1048 | 0.6357 | 0.4941 | 0.4041 | 0.6357 |
| 1.1364 | 184.6154 | 4800 | 1.1048 | 0.6357 | 0.4941 | 0.4041 | 0.6357 |
| 1.1207 | 188.4615 | 4900 | 1.1048 | 0.6357 | 0.4941 | 0.4041 | 0.6357 |
| 1.1302 | 192.3077 | 5000 | 1.1048 | 0.6357 | 0.4941 | 0.4041 | 0.6357 |
| 1.1235 | 196.1538 | 5100 | 1.1047 | 0.6357 | 0.4941 | 0.4041 | 0.6357 |
| 1.1236 | 200.0 | 5200 | 1.1047 | 0.6357 | 0.4941 | 0.4041 | 0.6357 |
### Framework versions
- Transformers 4.57.1
- Pytorch 2.8.0+cu126
- Datasets 4.4.1
- Tokenizers 0.22.1
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