metadata
tags:
- generated_from_trainer
datasets:
- null
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: electra-srb-ner
results:
- task:
name: Token Classification
type: token-classification
metric:
name: Accuracy
type: accuracy
value: 0.9418150723128973
electra-srb-ner
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2062
- Precision: 0.7553
- Recall: 0.7362
- F1: 0.7456
- Accuracy: 0.9418
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: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 207 | 0.2894 | 0.7278 | 0.6252 | 0.6726 | 0.9207 |
| No log | 2.0 | 414 | 0.2175 | 0.7463 | 0.6984 | 0.7216 | 0.9352 |
| 0.3035 | 3.0 | 621 | 0.2189 | 0.7826 | 0.7049 | 0.7417 | 0.9398 |
| 0.3035 | 4.0 | 828 | 0.2062 | 0.7553 | 0.7362 | 0.7456 | 0.9418 |
Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0
- Datasets 1.11.0
- Tokenizers 0.10.1