metadata
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner-ime
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: train
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.998195331607817
- name: Recall
type: recall
value: 0.9982190349544073
- name: F1
type: f1
value: 0.9982071831403979
- name: Accuracy
type: accuracy
value: 0.9979751132733664
bert-finetuned-ner-ime
This model is a fine-tuned version of snunlp/KR-BERT-char16424 on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0076
- Precision: 0.9982
- Recall: 0.9982
- F1: 0.9982
- Accuracy: 0.9980
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 |
|---|---|---|---|---|---|---|---|
| 0.0378 | 1.0 | 1756 | 0.0290 | 0.9934 | 0.9939 | 0.9936 | 0.9920 |
| 0.0214 | 2.0 | 3512 | 0.0138 | 0.9969 | 0.9970 | 0.9970 | 0.9965 |
| 0.0151 | 3.0 | 5268 | 0.0076 | 0.9982 | 0.9982 | 0.9982 | 0.9980 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.11.0