---
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-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-02_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: 0.0396
- Accuracy: 0.9875
- F1: 0.6321
- Precision: 0.6977
- Recall: 0.5777

## 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  | 0.0907          | 0.9814   | 0.0    | 0.0       | 0.0    |
| 0.0787        | 0.9862 | 2000  | 0.0707          | 0.9814   | 0.0    | 0.0       | 0.0    |
| 0.0628        | 1.4793 | 3000  | 0.0575          | 0.9822   | 0.1225 | 0.7682    | 0.0666 |
| 0.0537        | 1.9724 | 4000  | 0.0503          | 0.9842   | 0.3201 | 0.8086    | 0.1996 |
| 0.0478        | 2.4655 | 5000  | 0.0470          | 0.9851   | 0.4263 | 0.7606    | 0.2961 |
| 0.0453        | 2.9586 | 6000  | 0.0444          | 0.9858   | 0.4983 | 0.7270    | 0.3791 |
| 0.0389        | 3.4517 | 7000  | 0.0419          | 0.9864   | 0.5409 | 0.7312    | 0.4292 |
| 0.0393        | 3.9448 | 8000  | 0.0411          | 0.9863   | 0.5480 | 0.7138    | 0.4447 |
| 0.0349        | 4.4379 | 9000  | 0.0399          | 0.9868   | 0.5747 | 0.7203    | 0.4781 |
| 0.0344        | 4.9310 | 10000 | 0.0391          | 0.9870   | 0.5758 | 0.7380    | 0.4721 |
| 0.0302        | 5.4241 | 11000 | 0.0385          | 0.9871   | 0.5904 | 0.7254    | 0.4977 |
| 0.0305        | 5.9172 | 12000 | 0.0387          | 0.9871   | 0.5966 | 0.7152    | 0.5118 |
| 0.027         | 6.4103 | 13000 | 0.0384          | 0.9874   | 0.6057 | 0.7302    | 0.5174 |
| 0.0282        | 6.9034 | 14000 | 0.0381          | 0.9875   | 0.6079 | 0.7344    | 0.5186 |
| 0.0235        | 7.3964 | 15000 | 0.0385          | 0.9874   | 0.6181 | 0.7103    | 0.5471 |
| 0.0255        | 7.8895 | 16000 | 0.0382          | 0.9876   | 0.6257 | 0.7174    | 0.5548 |
| 0.0214        | 8.3826 | 17000 | 0.0382          | 0.9877   | 0.6353 | 0.7122    | 0.5734 |
| 0.0222        | 8.8757 | 18000 | 0.0388          | 0.9876   | 0.6282 | 0.7127    | 0.5615 |
| 0.0192        | 9.3688 | 19000 | 0.0396          | 0.9875   | 0.6321 | 0.6977    | 0.5777 |


### Framework versions

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