---
language:
- mn
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: mongolian-davlan-xlm-roberta-base-ner-hrl
  results: []
---

<!-- 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. -->

# mongolian-davlan-xlm-roberta-base-ner-hrl

This model is a fine-tuned version of [Davlan/xlm-roberta-base-ner-hrl](https://huggingface.co/Davlan/xlm-roberta-base-ner-hrl) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1166
- Precision: 0.9265
- Recall: 0.9340
- F1: 0.9303
- Accuracy: 0.9798

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.159         | 1.0   | 477  | 0.0792          | 0.9061    | 0.9167 | 0.9114 | 0.9764   |
| 0.0754        | 2.0   | 954  | 0.0721          | 0.9142    | 0.9257 | 0.9199 | 0.9783   |
| 0.0515        | 3.0   | 1431 | 0.0803          | 0.9189    | 0.9317 | 0.9253 | 0.9787   |
| 0.0374        | 4.0   | 1908 | 0.0758          | 0.9295    | 0.9353 | 0.9324 | 0.9804   |
| 0.0279        | 5.0   | 2385 | 0.0912          | 0.9267    | 0.9340 | 0.9304 | 0.9796   |
| 0.0214        | 6.0   | 2862 | 0.0993          | 0.9256    | 0.9325 | 0.9290 | 0.9796   |
| 0.0159        | 7.0   | 3339 | 0.1078          | 0.9237    | 0.9327 | 0.9282 | 0.9789   |
| 0.0122        | 8.0   | 3816 | 0.1108          | 0.9259    | 0.9347 | 0.9303 | 0.9797   |
| 0.0102        | 9.0   | 4293 | 0.1147          | 0.9263    | 0.9348 | 0.9305 | 0.9797   |
| 0.0077        | 10.0  | 4770 | 0.1166          | 0.9265    | 0.9340 | 0.9303 | 0.9798   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3