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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-03_train-01
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-03_train-01
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: 0.0401
- Accuracy: 0.9877
- F1: 0.6362
- Precision: 0.7046
- Recall: 0.5799
## 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 |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0941 | 0.4931 | 1000 | 0.0904 | 0.9814 | 0.0 | 0.0 | 0.0 |
| 0.0787 | 0.9862 | 2000 | 0.0705 | 0.9814 | 0.0 | 0.0 | 0.0 |
| 0.0628 | 1.4793 | 3000 | 0.0571 | 0.9822 | 0.1170 | 0.7402 | 0.0635 |
| 0.0537 | 1.9724 | 4000 | 0.0501 | 0.9842 | 0.3163 | 0.7991 | 0.1972 |
| 0.0478 | 2.4655 | 5000 | 0.0469 | 0.9851 | 0.4211 | 0.7561 | 0.2918 |
| 0.0453 | 2.9586 | 6000 | 0.0443 | 0.9857 | 0.4941 | 0.7238 | 0.3751 |
| 0.0389 | 3.4517 | 7000 | 0.0417 | 0.9863 | 0.5359 | 0.7234 | 0.4255 |
| 0.0393 | 3.9448 | 8000 | 0.0410 | 0.9862 | 0.5412 | 0.7034 | 0.4398 |
| 0.0349 | 4.4379 | 9000 | 0.0397 | 0.9868 | 0.5693 | 0.7206 | 0.4704 |
| 0.0344 | 4.9310 | 10000 | 0.0389 | 0.9870 | 0.5744 | 0.7307 | 0.4731 |
| 0.0302 | 5.4241 | 11000 | 0.0384 | 0.9872 | 0.5891 | 0.7262 | 0.4955 |
| 0.0305 | 5.9172 | 12000 | 0.0386 | 0.9870 | 0.5894 | 0.7087 | 0.5045 |
| 0.027 | 6.4103 | 13000 | 0.0384 | 0.9873 | 0.5966 | 0.7229 | 0.5079 |
| 0.0282 | 6.9034 | 14000 | 0.0380 | 0.9874 | 0.6018 | 0.7255 | 0.5141 |
| 0.0235 | 7.3964 | 15000 | 0.0382 | 0.9874 | 0.6185 | 0.7089 | 0.5485 |
| 0.0255 | 7.8895 | 16000 | 0.0380 | 0.9874 | 0.6198 | 0.7077 | 0.5512 |
| 0.0214 | 8.3826 | 17000 | 0.0382 | 0.9876 | 0.6292 | 0.7049 | 0.5681 |
| 0.0222 | 8.8757 | 18000 | 0.0386 | 0.9876 | 0.6271 | 0.7083 | 0.5626 |
| 0.0192 | 9.3688 | 19000 | 0.0397 | 0.9874 | 0.6294 | 0.6936 | 0.5761 |
| 0.0189 | 9.8619 | 20000 | 0.0396 | 0.9875 | 0.6300 | 0.6993 | 0.5732 |
| 0.0159 | 10.3550 | 21000 | 0.0401 | 0.9877 | 0.6362 | 0.7046 | 0.5799 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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