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w266_model2_BERT_LSTM_1
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.6673
- Accuracy: {'accuracy': 0.586}
- F1: {'f1': 0.5941271393567649}
- Precision: {'precision': 0.6305594263991693}
- Recall: {'recall': 0.586}
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: 5.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 125 | 2.7886 | {'accuracy': 0.563} | {'f1': 0.5737642190234387} | {'precision': 0.6070380044002861} | {'recall': 0.563} |
| No log | 2.0 | 250 | 3.2762 | {'accuracy': 0.567} | {'f1': 0.5732065475023022} | {'precision': 0.6124992011023714} | {'recall': 0.567} |
| No log | 3.0 | 375 | 3.1370 | {'accuracy': 0.57} | {'f1': 0.5799666523302439} | {'precision': 0.6122839339063632} | {'recall': 0.57} |
| 0.0465 | 4.0 | 500 | 3.3590 | {'accuracy': 0.569} | {'f1': 0.5796357806282344} | {'precision': 0.6093440842818532} | {'recall': 0.5689999999999998} |
| 0.0465 | 5.0 | 625 | 3.4285 | {'accuracy': 0.57} | {'f1': 0.580483223593091} | {'precision': 0.618976915416096} | {'recall': 0.57} |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.2
- Tokenizers 0.13.3
