metadata
library_name: transformers
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-cased-finetuned-ner-conll
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: 0.9360200160843535
- name: Recall
type: recall
value: 0.9397990310425265
- name: F1
type: f1
value: 0.9379057169718404
- name: Accuracy
type: accuracy
value: 0.984502855124507
bert-base-cased-finetuned-ner-conll
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: 0.0622
- Precision: 0.9360
- Recall: 0.9398
- F1: 0.9379
- Accuracy: 0.9845
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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.2423 | 1.0 | 878 | 0.0702 | 0.9089 | 0.9231 | 0.9160 | 0.9799 |
| 0.0476 | 2.0 | 1756 | 0.0675 | 0.9338 | 0.9315 | 0.9327 | 0.9833 |
| 0.0264 | 3.0 | 2634 | 0.0622 | 0.9360 | 0.9398 | 0.9379 | 0.9845 |
Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1