metadata
tags:
- generated_from_trainer
datasets:
- null
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: distilbert-srb-ner-setimes
results:
- task:
name: Token Classification
type: token-classification
metric:
name: Accuracy
type: accuracy
value: 0.9558538945331398
distilbert-srb-ner-setimes
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1509
- Precision: 0.7589
- Recall: 0.7883
- F1: 0.7733
- Accuracy: 0.9559
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: 32
- 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 | 104 | 0.2391 | 0.6625 | 0.6778 | 0.6701 | 0.9334 |
| No log | 2.0 | 208 | 0.1869 | 0.7314 | 0.7425 | 0.7369 | 0.9455 |
| No log | 3.0 | 312 | 0.1640 | 0.7513 | 0.7729 | 0.7620 | 0.9514 |
| No log | 4.0 | 416 | 0.1541 | 0.7606 | 0.7853 | 0.7728 | 0.9548 |
| 0.2087 | 5.0 | 520 | 0.1509 | 0.7589 | 0.7883 | 0.7733 | 0.9559 |
Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0
- Datasets 1.11.0
- Tokenizers 0.10.1