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# xlm-roberta-base-ner-demo

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1272
- Precision: 0.9267
- Recall: 0.9350
- F1: 0.9309
- Accuracy: 0.9786

## 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: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2011        | 1.0   | 477  | 0.0950          | 0.8951    | 0.9101 | 0.9025 | 0.9717   |
| 0.0809        | 2.0   | 954  | 0.1010          | 0.8992    | 0.9135 | 0.9063 | 0.9720   |
| 0.0588        | 3.0   | 1431 | 0.0937          | 0.9143    | 0.9274 | 0.9208 | 0.9765   |
| 0.0438        | 4.0   | 1908 | 0.0949          | 0.9192    | 0.9291 | 0.9241 | 0.9771   |
| 0.0316        | 5.0   | 2385 | 0.1000          | 0.9220    | 0.9300 | 0.9260 | 0.9771   |
| 0.0238        | 6.0   | 2862 | 0.1099          | 0.9266    | 0.9333 | 0.9299 | 0.9783   |
| 0.0181        | 7.0   | 3339 | 0.1125          | 0.9262    | 0.9344 | 0.9303 | 0.9783   |
| 0.0135        | 8.0   | 3816 | 0.1201          | 0.9220    | 0.9333 | 0.9276 | 0.9781   |
| 0.0106        | 9.0   | 4293 | 0.1244          | 0.9263    | 0.9343 | 0.9303 | 0.9784   |
| 0.0089        | 10.0  | 4770 | 0.1272          | 0.9267    | 0.9350 | 0.9309 | 0.9786   |


### Framework versions

- Transformers 4.35.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1

{"language": ["mn"], "license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "xlm-roberta-base", "model-index": [{"name": "xlm-roberta-base-ner-demo", "results": []}]}
transformers tensorboard safetensors xlm-roberta token-classification generated_from_trainer mn base_model:xlm-roberta-base license:mit autotrain_compatible endpoints_compatible region:us