metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: test-bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9354625186165811
- name: Recall
type: recall
value: 0.9513631773813531
- name: F1
type: f1
value: 0.943345848977889
- name: Accuracy
type: accuracy
value: 0.9867545770294931
test-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: 0.0600
- Precision: 0.9355
- Recall: 0.9514
- F1: 0.9433
- Accuracy: 0.9868
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 |
|---|---|---|---|---|---|---|---|
| 0.0849 | 1.0 | 1756 | 0.0713 | 0.9144 | 0.9366 | 0.9253 | 0.9817 |
| 0.0359 | 2.0 | 3512 | 0.0658 | 0.9346 | 0.9500 | 0.9422 | 0.9860 |
| 0.0206 | 3.0 | 5268 | 0.0600 | 0.9355 | 0.9514 | 0.9433 | 0.9868 |
Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.8.1+cu111
- Datasets 1.12.1.dev0
- Tokenizers 0.10.3