---
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-00_train-04
  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-04

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: 0.0374
- Accuracy: 0.9874
- F1: 0.6313
- Precision: 0.6913
- Recall: 0.5809

## 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 |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0934        | 0.4929 | 1000  | 0.0901          | 0.9815   | 0.0    | 0.0       | 0.0    |
| 0.0778        | 0.9857 | 2000  | 0.0700          | 0.9815   | 0.0    | 0.0       | 0.0    |
| 0.0618        | 1.4786 | 3000  | 0.0565          | 0.9829   | 0.1733 | 0.8388    | 0.0967 |
| 0.0535        | 1.9714 | 4000  | 0.0489          | 0.9844   | 0.3322 | 0.7953    | 0.2100 |
| 0.0473        | 2.4643 | 5000  | 0.0452          | 0.9857   | 0.4684 | 0.7474    | 0.3411 |
| 0.0436        | 2.9571 | 6000  | 0.0421          | 0.9862   | 0.4998 | 0.7577    | 0.3729 |
| 0.0389        | 3.4500 | 7000  | 0.0403          | 0.9866   | 0.5333 | 0.7517    | 0.4132 |
| 0.0376        | 3.9428 | 8000  | 0.0395          | 0.9867   | 0.5632 | 0.7194    | 0.4628 |
| 0.0339        | 4.4357 | 9000  | 0.0386          | 0.9869   | 0.5613 | 0.7431    | 0.4509 |
| 0.0337        | 4.9285 | 10000 | 0.0381          | 0.9873   | 0.5768 | 0.7512    | 0.4681 |
| 0.0295        | 5.4214 | 11000 | 0.0371          | 0.9872   | 0.6005 | 0.7141    | 0.5182 |
| 0.0305        | 5.9142 | 12000 | 0.0377          | 0.9874   | 0.5996 | 0.7307    | 0.5084 |
| 0.0254        | 6.4071 | 13000 | 0.0373          | 0.9875   | 0.6118 | 0.7224    | 0.5306 |
| 0.0273        | 6.9000 | 14000 | 0.0373          | 0.9876   | 0.6137 | 0.7237    | 0.5327 |
| 0.0228        | 7.3928 | 15000 | 0.0371          | 0.9878   | 0.6180 | 0.7331    | 0.5341 |
| 0.0235        | 7.8857 | 16000 | 0.0374          | 0.9874   | 0.6313 | 0.6913    | 0.5809 |


### Framework versions

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