---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-base-ka-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: wikiann
      type: wikiann
      config: ka
      split: validation
      args: ka
    metrics:
    - name: Precision
      type: precision
      value: 0.8505682876839947
    - name: Recall
      type: recall
      value: 0.8702816057519472
    - name: F1
      type: f1
      value: 0.8603120330609663
    - name: Accuracy
      type: accuracy
      value: 0.9424682155180856
language:
- ka
---

<!-- 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. -->

# xlm-roberta-base-ka-ner

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2031
- Precision: 0.8506
- Recall: 0.8703
- F1: 0.8603
- Accuracy: 0.9425

## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.5349        | 1.0   | 625  | 0.2377          | 0.8302    | 0.8218 | 0.8260 | 0.9287   |
| 0.2353        | 2.0   | 1250 | 0.2037          | 0.8556    | 0.8536 | 0.8546 | 0.9394   |
| 0.1782        | 3.0   | 1875 | 0.2031          | 0.8506    | 0.8703 | 0.8603 | 0.9425   |

## Metrics per category

{'LOC': {'precision': 0.8558191459670667,
  'recall': 0.9074874223142941,
  'f1': 0.8808962941683425,
  'number': 16895},
 'ORG': {'precision': 0.7917612346799818,
  'recall': 0.7510226049515608,
  'f1': 0.7708540492763231,
  'number': 9290},
 'PER': {'precision': 0.8896882494004796,
  'recall': 0.9157884743188076,
  'f1': 0.9025497076023392,
  'number': 10533},
 'overall_precision': 0.8505682876839947,
 'overall_recall': 0.8702816057519472,
 'overall_f1': 0.8603120330609663,
 'overall_accuracy': 0.9424682155180856}

 
### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0