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---
tags:
- generated_from_trainer
datasets:
- null
metrics:
- precision
- recall
- f1
- accuracy
language:
- sr
model_index:
- name: bert-srb-ner
results:
- task:
name: Token Classification
type: token-classification
metric:
name: Accuracy
type: accuracy
value: 0.9641060273510046
---
<!-- 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. -->
# bert-srb-ner
This model was finetuned from Aleksandar/bert-srb-cased-oscar on the setimes.SR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1647
- Precision: 0.8247
- Recall: 0.8454
- F1: 0.8349
- Accuracy: 0.9641
## Model description
Default settings for BERT model, finetuned with batch size of 16.
## Intended uses & limitations
| Tag (IOB) | Numerical representation | Meaning (Beginning = B., Inside = I.) |
|-------------|--------------------------|------------------------------------------|
| O | 0 | Other |
| B-per | 1 | B.Person |
| I-per | 2 | I. Person |
| B-org | 3 | B. organization |
| I-org | 4 | I. organization |
| B-loc | 5 | B. location |
| I-loc | 6 | I. location |
| B-misc | 7 | B. Miscellaneous |
| I-misc | 8 | I. Miscellaneous |
| B-deriv-per | 9 | B. Derived Person |
MIT license
## 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 | 0.2040 | 0.7006 | 0.7466 | 0.7228 | 0.9411 |
| No log | 2.0 | 414 | 0.1561 | 0.7299 | 0.7868 | 0.7573 | 0.9519 |
| 0.2313 | 3.0 | 621 | 0.1455 | 0.7693 | 0.7992 | 0.7840 | 0.9567 |
| 0.2313 | 4.0 | 828 | 0.1628 | 0.7760 | 0.8037 | 0.7896 | 0.9570 |
| 0.0828 | 5.0 | 1035 | 0.1424 | 0.7997 | 0.8299 | 0.8145 | 0.9604 |
| 0.0828 | 6.0 | 1242 | 0.1512 | 0.7983 | 0.8361 | 0.8168 | 0.9618 |
| 0.0828 | 7.0 | 1449 | 0.1587 | 0.8084 | 0.8415 | 0.8246 | 0.9627 |
| 0.0362 | 8.0 | 1656 | 0.1613 | 0.8154 | 0.8358 | 0.8255 | 0.9632 |
| 0.0362 | 9.0 | 1863 | 0.1685 | 0.8211 | 0.8429 | 0.8319 | 0.9632 |
| 0.0174 | 10.0 | 2070 | 0.1647 | 0.8247 | 0.8454 | 0.8349 | 0.9641 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0
- Datasets 1.11.0
- Tokenizers 0.10.1
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