metadata
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: NER_BERT_Finetune_Model
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.929726162982514
- name: Recall
type: recall
value: 0.9485021878155503
- name: F1
type: f1
value: 0.939020326557814
- name: Accuracy
type: accuracy
value: 0.9866809913463237
NER_BERT_Finetune_Model
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: 0.0598
- Precision: 0.9297
- Recall: 0.9485
- F1: 0.9390
- Accuracy: 0.9867
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 |
|---|---|---|---|---|---|---|---|
| 0.075 | 1.0 | 1756 | 0.0751 | 0.9139 | 0.9355 | 0.9246 | 0.9797 |
| 0.0397 | 2.0 | 3512 | 0.0583 | 0.9232 | 0.9463 | 0.9346 | 0.9849 |
| 0.024 | 3.0 | 5268 | 0.0598 | 0.9297 | 0.9485 | 0.9390 | 0.9867 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0