metadata
library_name: transformers
license: mit
base_model: roberta-base
tags:
- bert-ner-address
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: ner-results-3
results: []
ner-results-3
This model is a fine-tuned version of roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0132
- Precision: 0.9940
- Recall: 0.9950
- F1: 0.9945
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 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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|---|---|---|---|---|---|---|
| 0.0176 | 1.0 | 71551 | 0.0148 | 0.9932 | 0.9953 | 0.9943 |
| 0.008 | 2.0 | 143102 | 0.0108 | 0.9950 | 0.9958 | 0.9954 |
Framework versions
- Transformers 4.51.1
- Pytorch 2.5.1+cu124
- Datasets 3.5.0
- Tokenizers 0.21.0