metadata
base_model: HooshvareLab/bert-base-parsbert-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: output
results: []
language:
- fa
Persian Text Emotion Detection
This model is a fine-tuned version of 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