---
language:
- mn
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: mongolian-facebook-xlm-v-base-ner
  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-facebook-xlm-v-base-ner

This model is a fine-tuned version of [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1036
- Precision: 0.9263
- Recall: 0.9352
- F1: 0.9307
- Accuracy: 0.9792

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3409        | 1.0   | 477  | 0.1186          | 0.8832    | 0.9019 | 0.8924 | 0.9691   |
| 0.0953        | 2.0   | 954  | 0.0883          | 0.9130    | 0.9235 | 0.9182 | 0.9770   |
| 0.066         | 3.0   | 1431 | 0.0837          | 0.9166    | 0.9264 | 0.9215 | 0.9768   |
| 0.0487        | 4.0   | 1908 | 0.0918          | 0.9244    | 0.9286 | 0.9265 | 0.9778   |
| 0.0388        | 5.0   | 2385 | 0.0902          | 0.9218    | 0.9317 | 0.9268 | 0.9787   |
| 0.0304        | 6.0   | 2862 | 0.0955          | 0.9202    | 0.9296 | 0.9249 | 0.9780   |
| 0.0226        | 7.0   | 3339 | 0.0992          | 0.9226    | 0.9311 | 0.9269 | 0.9781   |
| 0.0192        | 8.0   | 3816 | 0.0962          | 0.9256    | 0.9328 | 0.9292 | 0.9790   |
| 0.0153        | 9.0   | 4293 | 0.1025          | 0.9243    | 0.9347 | 0.9295 | 0.9791   |
| 0.0133        | 10.0  | 4770 | 0.1036          | 0.9263    | 0.9352 | 0.9307 | 0.9792   |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3