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# 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
{"language": ["ka"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["wikiann"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "xlm-roberta-base", "model-index": [{"name": "xlm-roberta-base-ka-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wikiann", "type": "wikiann", "config": "ka", "split": "validation", "args": "ka"}, "metrics": [{"type": "precision", "value": 0.8505682876839947, "name": "Precision"}, {"type": "recall", "value": 0.8702816057519472, "name": "Recall"}, {"type": "f1", "value": 0.8603120330609663, "name": "F1"}, {"type": "accuracy", "value": 0.9424682155180856, "name": "Accuracy"}]}]}]}
transformers tensorboard safetensors xlm-roberta token-classification generated_from_trainer ka dataset:wikiann base_model:xlm-roberta-base license:mit model-index autotrain_compatible endpoints_compatible region:us