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---
tags:
- generated_from_trainer
datasets:
- null
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: electra-srb-ner-setimes
results:
- task:
name: Token Classification
type: token-classification
metric:
name: Accuracy
type: accuracy
value: 0.951370041268543
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# electra-srb-ner-setimes
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2619
- Precision: 0.8157
- Recall: 0.7934
- F1: 0.8044
- Accuracy: 0.9514
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 207 | 0.2845 | 0.7431 | 0.6314 | 0.6827 | 0.9225 |
| No log | 2.0 | 414 | 0.2082 | 0.7766 | 0.7134 | 0.7436 | 0.9396 |
| 0.2949 | 3.0 | 621 | 0.1992 | 0.7699 | 0.7596 | 0.7647 | 0.9439 |
| 0.2949 | 4.0 | 828 | 0.2044 | 0.7485 | 0.7908 | 0.7691 | 0.9456 |
| 0.0896 | 5.0 | 1035 | 0.2129 | 0.7827 | 0.7778 | 0.7802 | 0.9476 |
| 0.0896 | 6.0 | 1242 | 0.2330 | 0.7893 | 0.7882 | 0.7887 | 0.9485 |
| 0.0896 | 7.0 | 1449 | 0.2337 | 0.8026 | 0.7947 | 0.7986 | 0.9504 |
| 0.0334 | 8.0 | 1656 | 0.2579 | 0.8111 | 0.7850 | 0.7978 | 0.9503 |
| 0.0334 | 9.0 | 1863 | 0.2792 | 0.8263 | 0.7830 | 0.8041 | 0.9510 |
| 0.0152 | 10.0 | 2070 | 0.2619 | 0.8157 | 0.7934 | 0.8044 | 0.9514 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0
- Datasets 1.11.0
- Tokenizers 0.10.1
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