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: None
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9342388603611065
- name: Recall
type: recall
value: 0.9491753618310333
- name: F1
type: f1
value: 0.9416478837966441
- name: Accuracy
type: accuracy
value: 0.9862689115205746
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: 0.0636
- Precision: 0.9342
- Recall: 0.9492
- F1: 0.9416
- 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 |
|---|---|---|---|---|---|---|---|
| 0.0759 | 1.0 | 1756 | 0.0645 | 0.9062 | 0.9337 | 0.9198 | 0.9813 |
| 0.0371 | 2.0 | 3512 | 0.0652 | 0.9327 | 0.9473 | 0.9400 | 0.9857 |
| 0.0229 | 3.0 | 5268 | 0.0636 | 0.9342 | 0.9492 | 0.9416 | 0.9863 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1