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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-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-01_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: 0.0379
- Accuracy: 0.9877
- F1: 0.6213
- Precision: 0.7252
- Recall: 0.5434
## 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 | 0.0900 | 0.9814 | 0.0 | 0.0 | 0.0 |
| 0.0769 | 0.9862 | 2000 | 0.0688 | 0.9814 | 0.0015 | 0.9091 | 0.0008 |
| 0.0607 | 1.4793 | 3000 | 0.0561 | 0.9822 | 0.1054 | 0.8204 | 0.0563 |
| 0.0535 | 1.9724 | 4000 | 0.0501 | 0.9845 | 0.3683 | 0.7561 | 0.2434 |
| 0.0466 | 2.4655 | 5000 | 0.0451 | 0.9857 | 0.4853 | 0.7323 | 0.3629 |
| 0.0441 | 2.9586 | 6000 | 0.0423 | 0.9862 | 0.5089 | 0.7590 | 0.3827 |
| 0.0391 | 3.4517 | 7000 | 0.0406 | 0.9866 | 0.5538 | 0.7285 | 0.4467 |
| 0.0372 | 3.9448 | 8000 | 0.0395 | 0.9869 | 0.5537 | 0.7576 | 0.4362 |
| 0.0336 | 4.4379 | 9000 | 0.0387 | 0.9871 | 0.5704 | 0.7494 | 0.4604 |
| 0.0337 | 4.9310 | 10000 | 0.0381 | 0.9872 | 0.5865 | 0.7368 | 0.4871 |
| 0.0297 | 5.4241 | 11000 | 0.0374 | 0.9874 | 0.6051 | 0.7282 | 0.5175 |
| 0.0296 | 5.9172 | 12000 | 0.0383 | 0.9872 | 0.5796 | 0.7475 | 0.4732 |
| 0.0263 | 6.4103 | 13000 | 0.0381 | 0.9873 | 0.6096 | 0.7110 | 0.5335 |
| 0.0272 | 6.9034 | 14000 | 0.0380 | 0.9874 | 0.6193 | 0.7078 | 0.5505 |
| 0.0234 | 7.3964 | 15000 | 0.0379 | 0.9876 | 0.6178 | 0.7265 | 0.5373 |
| 0.0243 | 7.8895 | 16000 | 0.0379 | 0.9877 | 0.6213 | 0.7252 | 0.5434 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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