metadata
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-multilingual-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: MBERT_uncased_GeneralizedCrossEntropy_full_ft
results: []
MBERT_uncased_GeneralizedCrossEntropy_full_ft
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:
- Accuracy: 0.802
- F1: 0.8584
- Precision: 0.8310
- Recall: 0.8876
- Roc Auc: 0.7555
- Loss: 0.2729
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Accuracy | F1 | Precision | Recall | Roc Auc | Validation Loss |
|---|---|---|---|---|---|---|---|---|
| No log | 0.992 | 62 | 0.77 | 0.8487 | 0.7642 | 0.9541 | 0.6700 | 0.3336 |
| 0.3578 | 2.0 | 125 | 0.805 | 0.8567 | 0.8511 | 0.8624 | 0.7738 | 0.2695 |
| 0.3578 | 2.976 | 186 | 0.802 | 0.8584 | 0.8310 | 0.8876 | 0.7555 | 0.2729 |
Framework versions
- Transformers 4.45.2
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.20.3