metadata
license: apache-2.0
base_model: google/bert_uncased_L-2_H-128_A-2
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
- f1
model-index:
- name: bert-tiny-finetuned-enron-spam-detection
results: []
bert-tiny-finetuned-enron-spam-detection
This model is a fine-tuned version of google/bert_uncased_L-2_H-128_A-2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0633
- Precision: 0.9861
- Recall: 0.9851
- Accuracy: 0.9855
- F1: 0.9856
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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1 |
|---|---|---|---|---|---|---|---|
| 0.1163 | 1.0 | 1983 | 0.0847 | 0.9810 | 0.9722 | 0.9765 | 0.9766 |
| 0.0717 | 2.0 | 3966 | 0.0659 | 0.9784 | 0.9901 | 0.984 | 0.9842 |
| 0.0591 | 3.0 | 5949 | 0.0633 | 0.9861 | 0.9851 | 0.9855 | 0.9856 |
| 0.0452 | 4.0 | 7932 | 0.0647 | 0.9871 | 0.9831 | 0.985 | 0.9851 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3