metadata
license: apache-2.0
base_model: bert-base-cased
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
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9378109452736318
- name: Recall
type: recall
value: 0.9516997643890945
- name: F1
type: f1
value: 0.9447043100567992
- name: Accuracy
type: accuracy
value: 0.9872402425384117
library_name: transformers
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: 0.0572
- Precision: 0.9378
- Recall: 0.9517
- F1: 0.9447
- Accuracy: 0.9872
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 |
|---|---|---|---|---|---|---|---|
| 0.0762 | 1.0 | 1756 | 0.0663 | 0.9046 | 0.9303 | 0.9173 | 0.9808 |
| 0.0369 | 2.0 | 3512 | 0.0652 | 0.9352 | 0.9475 | 0.9413 | 0.9859 |
| 0.0225 | 3.0 | 5268 | 0.0572 | 0.9378 | 0.9517 | 0.9447 | 0.9872 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1