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---
library_name: transformers
license: mit
base_model: microsoft/deberta-v3-small
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: doc-topic-model_eval-04_train-00
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. -->
# doc-topic-model_eval-04_train-00
This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0377
- Accuracy: 0.9880
- F1: 0.6385
- Precision: 0.7205
- Recall: 0.5733
## 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-05
- train_batch_size: 4
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0929 | 0.4931 | 1000 | 0.0906 | 0.9815 | 0.0 | 0.0 | 0.0 |
| 0.0785 | 0.9862 | 2000 | 0.0704 | 0.9815 | 0.0 | 0.0 | 0.0 |
| 0.0622 | 1.4793 | 3000 | 0.0572 | 0.9823 | 0.1022 | 0.8442 | 0.0544 |
| 0.0542 | 1.9724 | 4000 | 0.0499 | 0.9843 | 0.3390 | 0.7662 | 0.2176 |
| 0.048 | 2.4655 | 5000 | 0.0459 | 0.9853 | 0.4278 | 0.7709 | 0.2960 |
| 0.0436 | 2.9586 | 6000 | 0.0429 | 0.9863 | 0.5135 | 0.7466 | 0.3913 |
| 0.0384 | 3.4517 | 7000 | 0.0411 | 0.9868 | 0.5512 | 0.7418 | 0.4386 |
| 0.0385 | 3.9448 | 8000 | 0.0396 | 0.9868 | 0.5391 | 0.7659 | 0.4159 |
| 0.0343 | 4.4379 | 9000 | 0.0392 | 0.9870 | 0.5622 | 0.7475 | 0.4505 |
| 0.0343 | 4.9310 | 10000 | 0.0383 | 0.9872 | 0.5747 | 0.7490 | 0.4662 |
| 0.0304 | 5.4241 | 11000 | 0.0381 | 0.9873 | 0.5883 | 0.7375 | 0.4894 |
| 0.0299 | 5.9172 | 12000 | 0.0367 | 0.9877 | 0.6116 | 0.7341 | 0.5242 |
| 0.0265 | 6.4103 | 13000 | 0.0374 | 0.9876 | 0.6157 | 0.7219 | 0.5367 |
| 0.0261 | 6.9034 | 14000 | 0.0365 | 0.9879 | 0.6179 | 0.7448 | 0.5279 |
| 0.0236 | 7.3964 | 15000 | 0.0374 | 0.9877 | 0.6228 | 0.7218 | 0.5476 |
| 0.0236 | 7.8895 | 16000 | 0.0372 | 0.9880 | 0.6263 | 0.7356 | 0.5453 |
| 0.0215 | 8.3826 | 17000 | 0.0376 | 0.9879 | 0.6326 | 0.7199 | 0.5642 |
| 0.0216 | 8.8757 | 18000 | 0.0381 | 0.9878 | 0.6322 | 0.7149 | 0.5666 |
| 0.0177 | 9.3688 | 19000 | 0.0377 | 0.9880 | 0.6385 | 0.7205 | 0.5733 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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