---
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-02_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-02_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: 0.0382
- Accuracy: 0.9879
- F1: 0.6370
- Precision: 0.7208
- Recall: 0.5707

## 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  | 0.0912          | 0.9814   | 0.0    | 0.0       | 0.0    |
| 0.0785        | 0.9862 | 2000  | 0.0708          | 0.9814   | 0.0    | 0.0       | 0.0    |
| 0.0622        | 1.4793 | 3000  | 0.0576          | 0.9823   | 0.1109 | 0.8665    | 0.0592 |
| 0.0542        | 1.9724 | 4000  | 0.0501          | 0.9842   | 0.3406 | 0.7715    | 0.2185 |
| 0.048         | 2.4655 | 5000  | 0.0461          | 0.9853   | 0.4277 | 0.7762    | 0.2952 |
| 0.0436        | 2.9586 | 6000  | 0.0434          | 0.9861   | 0.5112 | 0.7463    | 0.3887 |
| 0.0384        | 3.4517 | 7000  | 0.0414          | 0.9867   | 0.5496 | 0.7437    | 0.4358 |
| 0.0385        | 3.9448 | 8000  | 0.0402          | 0.9867   | 0.5363 | 0.7625    | 0.4136 |
| 0.0343        | 4.4379 | 9000  | 0.0396          | 0.9870   | 0.5633 | 0.7528    | 0.4500 |
| 0.0343        | 4.9310 | 10000 | 0.0388          | 0.9872   | 0.5772 | 0.7528    | 0.4681 |
| 0.0304        | 5.4241 | 11000 | 0.0388          | 0.9871   | 0.5816 | 0.7349    | 0.4812 |
| 0.0299        | 5.9172 | 12000 | 0.0374          | 0.9875   | 0.6071 | 0.7340    | 0.5176 |
| 0.0265        | 6.4103 | 13000 | 0.0377          | 0.9875   | 0.6135 | 0.7213    | 0.5337 |
| 0.0261        | 6.9034 | 14000 | 0.0372          | 0.9876   | 0.6117 | 0.7383    | 0.5221 |
| 0.0236        | 7.3964 | 15000 | 0.0377          | 0.9877   | 0.6207 | 0.7257    | 0.5423 |
| 0.0236        | 7.8895 | 16000 | 0.0377          | 0.9878   | 0.6228 | 0.7376    | 0.5389 |
| 0.0215        | 8.3826 | 17000 | 0.0379          | 0.9879   | 0.6336 | 0.7236    | 0.5634 |
| 0.0216        | 8.8757 | 18000 | 0.0382          | 0.9878   | 0.6330 | 0.7212    | 0.5640 |
| 0.0177        | 9.3688 | 19000 | 0.0382          | 0.9879   | 0.6370 | 0.7208    | 0.5707 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1