metadata
base_model: bert-base-cased
license: apache-2.0
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: NER
results: []
NER
This model is a fine-tuned version of bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0571
- Precision: 0.9540
- Recall: 0.9620
- F1: 0.9580
- Accuracy: 0.9812
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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0698 | 1.0 | 4031 | 0.0589 | 0.9537 | 0.9611 | 0.9574 | 0.9804 |
| 0.045 | 2.0 | 8062 | 0.0571 | 0.9540 | 0.9620 | 0.9580 | 0.9812 |
| 0.0289 | 3.0 | 12093 | 0.0633 | 0.9612 | 0.9597 | 0.9604 | 0.9819 |
Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1