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: 0.9589059598176599
bert-srb-ner-setimes
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1430
- Precision: 0.7811
- Recall: 0.8141
- F1: 0.7973
- Accuracy: 0.9589
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 | 0.2001 | 0.7109 | 0.7505 | 0.7301 | 0.9422 |
| No log | 2.0 | 414 | 0.1581 | 0.7347 | 0.7792 | 0.7563 | 0.9510 |
| 0.2354 | 3.0 | 621 | 0.1506 | 0.7612 | 0.8034 | 0.7818 | 0.9555 |
| 0.2354 | 4.0 | 828 | 0.1534 | 0.7728 | 0.8082 | 0.7901 | 0.9569 |
| 0.0883 | 5.0 | 1035 | 0.1430 | 0.7811 | 0.8141 | 0.7973 | 0.9589 |
Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0
- Datasets 1.11.0
- Tokenizers 0.10.1