---
library_name: transformers
base_model: microsoft/deberta-v3-small
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: doc-topic-model_eval-00_train-03
  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-00_train-03

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: 0.0375
- Accuracy: 0.9878
- F1: 0.6375
- Precision: 0.7079
- Recall: 0.5798

## 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 |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0923        | 0.4931 | 1000  | 0.0865          | 0.9815   | 0.0    | 0.0       | 0.0    |
| 0.0734        | 0.9862 | 2000  | 0.0668          | 0.9815   | 0.0    | 0.0       | 0.0    |
| 0.0606        | 1.4793 | 3000  | 0.0552          | 0.9824   | 0.1265 | 0.7701    | 0.0689 |
| 0.0532        | 1.9724 | 4000  | 0.0491          | 0.9841   | 0.2944 | 0.8178    | 0.1795 |
| 0.0466        | 2.4655 | 5000  | 0.0467          | 0.9851   | 0.4323 | 0.7342    | 0.3063 |
| 0.0433        | 2.9586 | 6000  | 0.0428          | 0.9859   | 0.4847 | 0.7565    | 0.3566 |
| 0.0391        | 3.4517 | 7000  | 0.0408          | 0.9866   | 0.5389 | 0.7450    | 0.4221 |
| 0.0378        | 3.9448 | 8000  | 0.0395          | 0.9867   | 0.5527 | 0.7365    | 0.4423 |
| 0.0338        | 4.4379 | 9000  | 0.0387          | 0.9870   | 0.5844 | 0.7160    | 0.4936 |
| 0.0333        | 4.9310 | 10000 | 0.0380          | 0.9871   | 0.5953 | 0.7094    | 0.5128 |
| 0.0301        | 5.4241 | 11000 | 0.0371          | 0.9876   | 0.6042 | 0.7368    | 0.5120 |
| 0.0292        | 5.9172 | 12000 | 0.0367          | 0.9877   | 0.6120 | 0.7381    | 0.5227 |
| 0.0266        | 6.4103 | 13000 | 0.0369          | 0.9879   | 0.6132 | 0.7535    | 0.5170 |
| 0.0257        | 6.9034 | 14000 | 0.0371          | 0.9877   | 0.6187 | 0.7257    | 0.5392 |
| 0.0229        | 7.3964 | 15000 | 0.0371          | 0.9880   | 0.6330 | 0.7279    | 0.5600 |
| 0.0238        | 7.8895 | 16000 | 0.0372          | 0.9879   | 0.6306 | 0.7250    | 0.5579 |
| 0.0198        | 8.3826 | 17000 | 0.0375          | 0.9878   | 0.6375 | 0.7079    | 0.5798 |


### Framework versions

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