metadata
license: apache-2.0
base_model: bert-base-uncased
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: 0.9271228359439406
- name: Recall
type: recall
value: 0.9463143722652305
- name: F1
type: f1
value: 0.9366203048221869
- name: Accuracy
type: accuracy
value: 0.9868937360001271
bert-finetuned-ner
This model is a fine-tuned version of bert-base-uncased on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0532
- Precision: 0.9271
- Recall: 0.9463
- F1: 0.9366
- Accuracy: 0.9869
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: 16
- eval_batch_size: 16
- 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 |
|---|---|---|---|---|---|---|---|
| 0.2141 | 1.0 | 878 | 0.0595 | 0.9003 | 0.9305 | 0.9152 | 0.9836 |
| 0.0449 | 2.0 | 1756 | 0.0529 | 0.9236 | 0.9455 | 0.9344 | 0.9861 |
| 0.0243 | 3.0 | 2634 | 0.0532 | 0.9271 | 0.9463 | 0.9366 | 0.9869 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1