---
base_model: HooshvareLab/bert-base-parsbert-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: output
  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. -->

# Persian Text Emotion Detection

This model is a fine-tuned version of [HooshvareLab/bert-base-parsbert-uncased](https://huggingface.co/HooshvareLab/bert-base-parsbert-uncased) on a custom dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2551
- Precision: 0.9362
- Recall: 0.9360
- Fscore: 0.9359
- Accuracy: 0.9360

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Fscore | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 348  | 0.3054          | 0.9166    | 0.9144 | 0.9136 | 0.9144   |
| 0.5158        | 2.0   | 696  | 0.2551          | 0.9362    | 0.9360 | 0.9359 | 0.9360   |
| 0.1469        | 3.0   | 1044 | 0.3670          | 0.9283    | 0.9259 | 0.9245 | 0.9259   |
| 0.1469        | 4.0   | 1392 | 0.3833          | 0.9331    | 0.9317 | 0.9307 | 0.9317   |
| 0.0453        | 5.0   | 1740 | 0.4241          | 0.9356    | 0.9345 | 0.9342 | 0.9345   |
| 0.0237        | 6.0   | 2088 | 0.3750          | 0.9441    | 0.9439 | 0.9437 | 0.9439   |
| 0.0237        | 7.0   | 2436 | 0.3986          | 0.9389    | 0.9388 | 0.9385 | 0.9388   |
| 0.009         | 8.0   | 2784 | 0.4100          | 0.9407    | 0.9403 | 0.9397 | 0.9403   |
| 0.0053        | 9.0   | 3132 | 0.4005          | 0.9403    | 0.9403 | 0.9401 | 0.9403   |
| 0.0053        | 10.0  | 3480 | 0.3986          | 0.9410    | 0.9410 | 0.9408 | 0.9410   |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3