metadata
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9382041086812458
- name: Recall
type: recall
value: 0.9530461124200605
- name: F1
type: f1
value: 0.9455668725997661
- name: Accuracy
type: accuracy
value: 0.9863130629304763
bert-finetuned-ner
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0632
- Precision: 0.9382
- Recall: 0.9530
- F1: 0.9456
- Accuracy: 0.9863
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: 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 |
|---|---|---|---|---|---|---|---|
| 0.0778 | 1.0 | 1756 | 0.0629 | 0.9111 | 0.9362 | 0.9235 | 0.9830 |
| 0.0354 | 2.0 | 3512 | 0.0727 | 0.9332 | 0.9446 | 0.9389 | 0.9842 |
| 0.0229 | 3.0 | 5268 | 0.0632 | 0.9382 | 0.9530 | 0.9456 | 0.9863 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1