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---
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: electra-srb-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wikiann
type: wikiann
args: sr
metric:
name: Accuracy
type: accuracy
value: 0.95641898994996
---
<!-- 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 wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3017
- Precision: 0.8911
- Recall: 0.9081
- F1: 0.8995
- Accuracy: 0.9564
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2535 | 1.0 | 1250 | 0.2015 | 0.8494 | 0.8605 | 0.8549 | 0.9376 |
| 0.1461 | 2.0 | 2500 | 0.1853 | 0.8800 | 0.8681 | 0.8740 | 0.9464 |
| 0.0914 | 3.0 | 3750 | 0.2022 | 0.8695 | 0.8912 | 0.8802 | 0.9485 |
| 0.0545 | 4.0 | 5000 | 0.2214 | 0.8758 | 0.8975 | 0.8865 | 0.9514 |
| 0.0385 | 5.0 | 6250 | 0.2536 | 0.8806 | 0.9010 | 0.8907 | 0.9523 |
| 0.0266 | 6.0 | 7500 | 0.2506 | 0.8834 | 0.9020 | 0.8926 | 0.9539 |
| 0.0133 | 7.0 | 8750 | 0.2745 | 0.8910 | 0.9057 | 0.8983 | 0.9562 |
| 0.0077 | 8.0 | 10000 | 0.2946 | 0.8872 | 0.9065 | 0.8968 | 0.9559 |
| 0.0043 | 9.0 | 11250 | 0.2931 | 0.8902 | 0.9094 | 0.8997 | 0.9567 |
| 0.0022 | 10.0 | 12500 | 0.3017 | 0.8911 | 0.9081 | 0.8995 | 0.9564 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0
- Datasets 1.11.0
- Tokenizers 0.10.1
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