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NLP_Model_Classifier
This model is a fine-tuned version of HooshvareLab/bert-base-parsbert-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5617
- Accuracy: {'accuracy': 0.912962962962963}
- F1 Weighted: {'f1': 0.9141126838691339}
- F1: {'f1': 0.912962962962963}
- Recall: {'recall': 0.912962962962963}
- Precision: {'precision': 0.912962962962963}
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Weighted | F1 | Recall | Precision |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 270 | 0.4435 | {'accuracy': 0.8851851851851852} | {'f1': 0.8837658106568329} | {'f1': 0.8851851851851851} | {'recall': 0.8851851851851852} | {'precision': 0.8851851851851852} |
| 0.6248 | 2.0 | 540 | 0.4601 | {'accuracy': 0.9018518518518519} | {'f1': 0.9003935312696059} | {'f1': 0.9018518518518519} | {'recall': 0.9018518518518519} | {'precision': 0.9018518518518519} |
| 0.6248 | 3.0 | 810 | 0.5067 | {'accuracy': 0.9018518518518519} | {'f1': 0.903263191345543} | {'f1': 0.9018518518518519} | {'recall': 0.9018518518518519} | {'precision': 0.9018518518518519} |
| 0.0931 | 4.0 | 1080 | 0.5500 | {'accuracy': 0.912962962962963} | {'f1': 0.9137945012321058} | {'f1': 0.912962962962963} | {'recall': 0.912962962962963} | {'precision': 0.912962962962963} |
| 0.0931 | 5.0 | 1350 | 0.5617 | {'accuracy': 0.912962962962963} | {'f1': 0.9141126838691339} | {'f1': 0.912962962962963} | {'recall': 0.912962962962963} | {'precision': 0.912962962962963} |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
