metadata
library_name: transformers
license: mit
base_model: roberta-base
tags:
- bert-ner-address-1
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: results
results: []
results
This model is a fine-tuned version of roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0207
- Precision: 0.9947
- Recall: 0.9949
- F1: 0.9948
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: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Use 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 |
|---|---|---|---|---|---|---|
| 0.0274 | 1.0 | 35645 | 0.0271 | 0.9881 | 0.9915 | 0.9898 |
| 0.0424 | 2.0 | 71290 | 0.0244 | 0.9935 | 0.9941 | 0.9938 |
| 0.0162 | 3.0 | 106935 | 0.0218 | 0.9945 | 0.9947 | 0.9946 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.4.0
- Datasets 3.1.0
- Tokenizers 0.20.3