metadata
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-uncased-finetuned-ner-increased
results: []
bert-base-uncased-finetuned-ner-increased
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0086
- Precision: 0.9921
- Recall: 0.9912
- F1: 0.9917
- Accuracy: 0.9975
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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0451 | 0.9995 | 937 | 0.0108 | 0.9893 | 0.9868 | 0.9881 | 0.9965 |
| 0.0079 | 2.0 | 1875 | 0.0092 | 0.9913 | 0.9886 | 0.9899 | 0.9970 |
| 0.0043 | 2.9984 | 2811 | 0.0094 | 0.9923 | 0.9901 | 0.9912 | 0.9974 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1