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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-02
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-02
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: 0.0374
- Accuracy: 0.9879
- F1: 0.6272
- Precision: 0.7299
- Recall: 0.5498
## 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 |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0944 | 0.4931 | 1000 | 0.0895 | 0.9815 | 0.0 | 0.0 | 0.0 |
| 0.0769 | 0.9862 | 2000 | 0.0685 | 0.9815 | 0.0014 | 1.0 | 0.0007 |
| 0.0607 | 1.4793 | 3000 | 0.0560 | 0.9823 | 0.1066 | 0.8022 | 0.0571 |
| 0.0535 | 1.9724 | 4000 | 0.0501 | 0.9846 | 0.3748 | 0.7494 | 0.2498 |
| 0.0466 | 2.4655 | 5000 | 0.0450 | 0.9858 | 0.4899 | 0.7338 | 0.3677 |
| 0.0441 | 2.9586 | 6000 | 0.0421 | 0.9863 | 0.5084 | 0.7553 | 0.3832 |
| 0.0391 | 3.4517 | 7000 | 0.0404 | 0.9868 | 0.5581 | 0.7311 | 0.4513 |
| 0.0372 | 3.9448 | 8000 | 0.0393 | 0.9870 | 0.5568 | 0.7564 | 0.4405 |
| 0.0336 | 4.4379 | 9000 | 0.0382 | 0.9872 | 0.5749 | 0.7485 | 0.4666 |
| 0.0337 | 4.9310 | 10000 | 0.0375 | 0.9874 | 0.5938 | 0.7375 | 0.4970 |
| 0.0297 | 5.4241 | 11000 | 0.0368 | 0.9875 | 0.6079 | 0.7260 | 0.5228 |
| 0.0296 | 5.9172 | 12000 | 0.0376 | 0.9875 | 0.5899 | 0.7526 | 0.4850 |
| 0.0263 | 6.4103 | 13000 | 0.0372 | 0.9877 | 0.6211 | 0.7210 | 0.5455 |
| 0.0272 | 6.9034 | 14000 | 0.0376 | 0.9875 | 0.6194 | 0.7061 | 0.5516 |
| 0.0234 | 7.3964 | 15000 | 0.0373 | 0.9878 | 0.6222 | 0.7304 | 0.5420 |
| 0.0243 | 7.8895 | 16000 | 0.0374 | 0.9879 | 0.6272 | 0.7299 | 0.5498 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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