metadata
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-multilingual-uncased
tags:
- generated_from_trainer
metrics:
- f1
- precision
model-index:
- name: bert-fraud-classification-test-mass
results: []
bert-fraud-classification-test-mass
This model is a fine-tuned version of google-bert/bert-base-multilingual-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3963
- F1: 0.8194
- Precision: 0.8445
- Val Accuracy: 0.8375
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: 44
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 88
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 2
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Val Accuracy |
|---|---|---|---|---|---|---|
| 0.5197 | 0.1743 | 40 | 0.5468 | 0.7488 | 0.6907 | 0.7459 |
| 0.5208 | 0.3486 | 80 | 0.4667 | 0.7687 | 0.7890 | 0.7911 |
| 0.4235 | 0.5229 | 120 | 0.4351 | 0.7986 | 0.7898 | 0.8113 |
| 0.404 | 0.6972 | 160 | 0.4577 | 0.7972 | 0.7751 | 0.8066 |
| 0.3736 | 0.8715 | 200 | 0.4274 | 0.7914 | 0.8775 | 0.8240 |
| 0.419 | 1.0458 | 240 | 0.4058 | 0.7912 | 0.8737 | 0.8232 |
| 0.2701 | 1.2200 | 280 | 0.4075 | 0.8124 | 0.8393 | 0.8316 |
| 0.4345 | 1.3943 | 320 | 0.4246 | 0.8110 | 0.8088 | 0.8244 |
| 0.3258 | 1.5686 | 360 | 0.4023 | 0.7992 | 0.8788 | 0.8294 |
| 0.3938 | 1.7429 | 400 | 0.3945 | 0.8174 | 0.8447 | 0.8361 |
| 0.2529 | 1.9172 | 440 | 0.3963 | 0.8194 | 0.8445 | 0.8375 |
Framework versions
- Transformers 4.46.0.dev0
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0