metadata
license: apache-2.0
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
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9329136988570482
- name: Recall
type: recall
value: 0.9478290138000673
- name: F1
type: f1
value: 0.9403122130394858
- name: Accuracy
type: accuracy
value: 0.9855477718255137
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: 0.0663
- Precision: 0.9329
- Recall: 0.9478
- F1: 0.9403
- Accuracy: 0.9855
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 |
|---|---|---|---|---|---|---|---|
| 0.0837 | 1.0 | 1756 | 0.0656 | 0.9151 | 0.9392 | 0.9270 | 0.9834 |
| 0.0388 | 2.0 | 3512 | 0.0619 | 0.9249 | 0.9475 | 0.9361 | 0.9855 |
| 0.0198 | 3.0 | 5268 | 0.0663 | 0.9329 | 0.9478 | 0.9403 | 0.9855 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1