---
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