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: 0.0881
- Precision: 0.8860
- Recall: 0.9197
- F1: 0.9026
- Accuracy: 0.9801
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 with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0671 | 1.0 | 1250 | 0.0929 | 0.8681 | 0.9150 | 0.8909 | 0.9774 |
| 0.0428 | 2.0 | 2500 | 0.0871 | 0.8909 | 0.9177 | 0.9041 | 0.9800 |
| 0.0373 | 3.0 | 3750 | 0.0881 | 0.8860 | 0.9197 | 0.9026 | 0.9801 |
Framework versions
- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1