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---
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: 0.9509610737256943
---
<!-- 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
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2686
- Precision: 0.8132
- Recall: 0.7889
- F1: 0.8009
- Accuracy: 0.9510
## 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 | 0.2887 | 0.7392 | 0.6269 | 0.6784 | 0.9221 |
| No log | 2.0 | 414 | 0.2076 | 0.7690 | 0.7147 | 0.7409 | 0.9397 |
| 0.2949 | 3.0 | 621 | 0.2011 | 0.7698 | 0.7583 | 0.7640 | 0.9441 |
| 0.2949 | 4.0 | 828 | 0.2077 | 0.7600 | 0.7807 | 0.7702 | 0.9451 |
| 0.089 | 5.0 | 1035 | 0.2198 | 0.7884 | 0.7684 | 0.7783 | 0.9465 |
| 0.089 | 6.0 | 1242 | 0.2437 | 0.7885 | 0.7824 | 0.7854 | 0.9474 |
| 0.089 | 7.0 | 1449 | 0.2394 | 0.7986 | 0.7970 | 0.7978 | 0.9511 |
| 0.0322 | 8.0 | 1656 | 0.2675 | 0.8135 | 0.7775 | 0.7951 | 0.9497 |
| 0.0322 | 9.0 | 1863 | 0.2832 | 0.8161 | 0.7798 | 0.7975 | 0.9508 |
| 0.0141 | 10.0 | 2070 | 0.2686 | 0.8132 | 0.7889 | 0.8009 | 0.9510 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0
- Datasets 1.11.0
- Tokenizers 0.10.1
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