metadata
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: w266_model3_BERT_CNN
results: []
w266_model3_BERT_CNN
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: 0.7935
- Accuracy: {'accuracy': 0.67}
- F1: {'f1': 0.6539863523155215}
- Precision: {'precision': 0.6655888523241464}
- Recall: {'recall': 0.67}
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 |
|---|---|---|---|---|---|---|---|
| 0.7881 | 1.0 | 1923 | 0.8177 | {'accuracy': 0.638} | {'f1': 0.6219209356584174} | {'precision': 0.6325213408748697} | {'recall': 0.638} |
| 0.649 | 2.0 | 3846 | 0.8257 | {'accuracy': 0.669} | {'f1': 0.6701535233107099} | {'precision': 0.672307962349643} | {'recall': 0.669} |
| 0.4771 | 3.0 | 5769 | 0.8922 | {'accuracy': 0.676} | {'f1': 0.6778795418743319} | {'precision': 0.6805694646691987} | {'recall': 0.676} |
| 0.3403 | 4.0 | 7692 | 1.4285 | {'accuracy': 0.669} | {'f1': 0.666176554548987} | {'precision': 0.6653390405441227} | {'recall': 0.669} |
| 0.2088 | 5.0 | 9615 | 1.7417 | {'accuracy': 0.67} | {'f1': 0.6716636513157895} | {'precision': 0.6752339933799478} | {'recall': 0.67} |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3