metadata
tags:
- generated_from_trainer
datasets:
- null
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: bert-srb-ner-setimes
results:
- task:
name: Token Classification
type: token-classification
metric:
name: Accuracy
type: accuracy
value: 0.95951375991896
bert-srb-ner-setimes
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1422
- Precision: 0.7886
- Recall: 0.8150
- F1: 0.8016
- Accuracy: 0.9595
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: 8
- 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 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 207 | 0.2017 | 0.7113 | 0.7389 | 0.7249 | 0.9419 |
| No log | 2.0 | 414 | 0.1594 | 0.7272 | 0.7787 | 0.7521 | 0.9510 |
| 0.233 | 3.0 | 621 | 0.1476 | 0.7576 | 0.8020 | 0.7792 | 0.9560 |
| 0.233 | 4.0 | 828 | 0.1471 | 0.7782 | 0.8130 | 0.7952 | 0.9582 |
| 0.0888 | 5.0 | 1035 | 0.1422 | 0.7886 | 0.8150 | 0.8016 | 0.9595 |
Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0
- Datasets 1.11.0
- Tokenizers 0.10.1