metadata
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-tiny-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.8083060109289617
- name: Recall
type: recall
value: 0.8273856136033113
- name: F1
type: f1
value: 0.8177345348001547
- name: Accuracy
type: accuracy
value: 0.9597597979252387
bert-tiny-finetuned-ner
This model is a fine-tuned version of prajjwal1/bert-tiny on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.1689
- Precision: 0.8083
- Recall: 0.8274
- F1: 0.8177
- Accuracy: 0.9598
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: 5
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
0.0355 | 1.0 | 878 | 0.1692 | 0.8072 | 0.8248 | 0.8159 | 0.9594 |
0.0411 | 2.0 | 1756 | 0.1678 | 0.8101 | 0.8277 | 0.8188 | 0.9600 |
0.0386 | 3.0 | 2634 | 0.1697 | 0.8103 | 0.8269 | 0.8186 | 0.9599 |
0.0373 | 4.0 | 3512 | 0.1694 | 0.8106 | 0.8263 | 0.8183 | 0.9600 |
0.0383 | 5.0 | 4390 | 0.1689 | 0.8083 | 0.8274 | 0.8177 | 0.9598 |
Framework versions
- Transformers 4.10.0
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3