metadata
library_name: transformers
license: mit
base_model: BAAI/bge-small-en-v1.5
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results: []
bert-finetuned-ner
This model is a fine-tuned version of BAAI/bge-small-en-v1.5 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0890
- Precision: 0.9050
- Recall: 0.9287
- F1: 0.9167
- Accuracy: 0.9828
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
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0647 | 1.0 | 1250 | 0.0937 | 0.8574 | 0.9124 | 0.8840 | 0.9769 |
| 0.0465 | 2.0 | 2500 | 0.0914 | 0.8914 | 0.9156 | 0.9033 | 0.9802 |
| 0.0351 | 3.0 | 3750 | 0.0871 | 0.8950 | 0.9168 | 0.9058 | 0.9814 |
| 0.0298 | 4.0 | 5000 | 0.0891 | 0.8966 | 0.9262 | 0.9111 | 0.9816 |
| 0.025 | 5.0 | 6250 | 0.0888 | 0.8962 | 0.9282 | 0.9119 | 0.9819 |
| 0.0193 | 6.0 | 7500 | 0.0836 | 0.9068 | 0.9291 | 0.9178 | 0.9827 |
| 0.0165 | 7.0 | 8750 | 0.0874 | 0.9051 | 0.9292 | 0.9170 | 0.9829 |
| 0.0132 | 8.0 | 10000 | 0.0890 | 0.9050 | 0.9287 | 0.9167 | 0.9828 |
Framework versions
- Transformers 4.56.2
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1