---
language:
- mn
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-base-ner-hrl-ner-finetuning
  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. -->

# xlm-roberta-base-ner-hrl-ner-finetuning

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: 0.1135
- Precision: 0.9290
- Recall: 0.9367
- F1: 0.9328
- Accuracy: 0.9801

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1534        | 1.0   | 477  | 0.0870          | 0.9001    | 0.9124 | 0.9062 | 0.9740   |
| 0.077         | 2.0   | 954  | 0.0764          | 0.9187    | 0.9321 | 0.9253 | 0.9789   |
| 0.0529        | 3.0   | 1431 | 0.0845          | 0.9178    | 0.9313 | 0.9245 | 0.9791   |
| 0.0377        | 4.0   | 1908 | 0.0805          | 0.9200    | 0.9310 | 0.9255 | 0.9795   |
| 0.0292        | 5.0   | 2385 | 0.0918          | 0.9278    | 0.9346 | 0.9312 | 0.9795   |
| 0.0204        | 6.0   | 2862 | 0.1016          | 0.9222    | 0.9323 | 0.9273 | 0.9790   |
| 0.0167        | 7.0   | 3339 | 0.1066          | 0.9271    | 0.9327 | 0.9299 | 0.9790   |
| 0.0134        | 8.0   | 3816 | 0.1088          | 0.9253    | 0.9358 | 0.9305 | 0.9797   |
| 0.0101        | 9.0   | 4293 | 0.1134          | 0.9289    | 0.9357 | 0.9323 | 0.9798   |
| 0.0079        | 10.0  | 4770 | 0.1135          | 0.9290    | 0.9367 | 0.9328 | 0.9801   |


### Framework versions

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