metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: bert-large-uncased-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metric:
name: Accuracy
type: accuracy
value: 0.9877039414110284
bert-large-uncased-ner
This model is a fine-tuned version of bert-large-uncased on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0591
- Precision: 0.9465
- Recall: 0.9568
- F1: 0.9517
- Accuracy: 0.9877
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: 5e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.1702 | 1.0 | 878 | 0.0578 | 0.9202 | 0.9347 | 0.9274 | 0.9836 |
| 0.0392 | 2.0 | 1756 | 0.0601 | 0.9306 | 0.9448 | 0.9377 | 0.9851 |
| 0.0157 | 3.0 | 2634 | 0.0517 | 0.9405 | 0.9544 | 0.9474 | 0.9875 |
| 0.0057 | 4.0 | 3512 | 0.0591 | 0.9465 | 0.9568 | 0.9517 | 0.9877 |
Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3