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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-01_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-01_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: 0.0386
- Accuracy: 0.9878
- F1: 0.6354
- Precision: 0.7155
- Recall: 0.5714
## 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 | 0.0911 | 0.9814 | 0.0 | 0.0 | 0.0 |
| 0.0785 | 0.9862 | 2000 | 0.0707 | 0.9814 | 0.0 | 0.0 | 0.0 |
| 0.0622 | 1.4793 | 3000 | 0.0574 | 0.9823 | 0.1057 | 0.8595 | 0.0563 |
| 0.0542 | 1.9724 | 4000 | 0.0498 | 0.9843 | 0.3396 | 0.7800 | 0.2171 |
| 0.048 | 2.4655 | 5000 | 0.0461 | 0.9852 | 0.4251 | 0.7708 | 0.2935 |
| 0.0436 | 2.9586 | 6000 | 0.0433 | 0.9860 | 0.5031 | 0.7426 | 0.3804 |
| 0.0384 | 3.4517 | 7000 | 0.0413 | 0.9865 | 0.5389 | 0.7357 | 0.4252 |
| 0.0385 | 3.9448 | 8000 | 0.0399 | 0.9867 | 0.5362 | 0.7647 | 0.4128 |
| 0.0343 | 4.4379 | 9000 | 0.0396 | 0.9869 | 0.5599 | 0.7452 | 0.4484 |
| 0.0343 | 4.9310 | 10000 | 0.0387 | 0.9870 | 0.5692 | 0.7471 | 0.4598 |
| 0.0304 | 5.4241 | 11000 | 0.0385 | 0.9873 | 0.5861 | 0.7432 | 0.4837 |
| 0.0299 | 5.9172 | 12000 | 0.0373 | 0.9875 | 0.6055 | 0.7342 | 0.5152 |
| 0.0265 | 6.4103 | 13000 | 0.0376 | 0.9873 | 0.6069 | 0.7159 | 0.5268 |
| 0.0261 | 6.9034 | 14000 | 0.0372 | 0.9877 | 0.6138 | 0.7384 | 0.5252 |
| 0.0236 | 7.3964 | 15000 | 0.0378 | 0.9876 | 0.6187 | 0.7225 | 0.5409 |
| 0.0236 | 7.8895 | 16000 | 0.0379 | 0.9878 | 0.6205 | 0.7374 | 0.5357 |
| 0.0215 | 8.3826 | 17000 | 0.0383 | 0.9876 | 0.6241 | 0.7126 | 0.5551 |
| 0.0216 | 8.8757 | 18000 | 0.0386 | 0.9877 | 0.6297 | 0.7143 | 0.5630 |
| 0.0177 | 9.3688 | 19000 | 0.0386 | 0.9878 | 0.6354 | 0.7155 | 0.5714 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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