metadata
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-cased
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: 0.9245192191975353
- name: Recall
type: recall
value: 0.9319212946114467
- name: F1
type: f1
value: 0.9282054999758349
- name: Accuracy
type: accuracy
value: 0.9332577853652794
bert-base-cased
This model was trained from scratch on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.3045
- Precision: 0.9245
- Recall: 0.9319
- F1: 0.9282
- Accuracy: 0.9333
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 |
|---|---|---|---|---|---|---|---|
| 0.2707 | 1.0 | 1756 | 0.3120 | 0.9171 | 0.9263 | 0.9217 | 0.9267 |
| 0.1829 | 2.0 | 3512 | 0.2928 | 0.9189 | 0.9295 | 0.9242 | 0.9299 |
| 0.1411 | 3.0 | 5268 | 0.3045 | 0.9245 | 0.9319 | 0.9282 | 0.9333 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3