metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
base_model: bert-base-cased
model-index:
- name: bert-finetuned-ner
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- type: precision
value: 0.9328493647912885
name: Precision
- type: recall
value: 0.9515314708852238
name: Recall
- type: f1
value: 0.942097808881113
name: F1
- type: accuracy
value: 0.9865632542532525
name: Accuracy
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: 0.0591
- Precision: 0.9328
- Recall: 0.9515
- F1: 0.9421
- Accuracy: 0.9866
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 |
|---|---|---|---|---|---|---|---|
| 0.088 | 1.0 | 1756 | 0.0673 | 0.9190 | 0.9334 | 0.9261 | 0.9823 |
| 0.0346 | 2.0 | 3512 | 0.0611 | 0.9284 | 0.9477 | 0.9380 | 0.9855 |
| 0.0178 | 3.0 | 5268 | 0.0591 | 0.9328 | 0.9515 | 0.9421 | 0.9866 |
Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2