SeyedAli/Persian-Text-Emotion-Bert-V1

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