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-base-uncased-conll2003
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: test
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.8885217391304348
- name: Recall
type: recall
value: 0.9045679886685553
- name: F1
type: f1
value: 0.8964730654500789
- name: Accuracy
type: accuracy
value: 0.9781414881016475
bert-base-uncased-conll2003
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: 0.1530
- Precision: 0.8885
- Recall: 0.9046
- F1: 0.8965
- Accuracy: 0.9781
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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0651 | 1.0 | 3922 | 0.1483 | 0.8842 | 0.9067 | 0.8953 | 0.9775 |
| 0.0287 | 2.0 | 7844 | 0.1530 | 0.8885 | 0.9046 | 0.8965 | 0.9781 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.2.2
- Datasets 2.20.0
- Tokenizers 0.13.3