---
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