---
language:
- mn
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bloom-mongolian-ner-demo
  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. -->

# bloom-mongolian-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: 0.1048
- Precision: 0.9267
- Recall: 0.9354
- F1: 0.9310
- Accuracy: 0.9796

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.195         | 1.0   | 477  | 0.0947          | 0.8845    | 0.8994 | 0.8919 | 0.9707   |
| 0.0848        | 2.0   | 954  | 0.0761          | 0.9095    | 0.9235 | 0.9164 | 0.9774   |
| 0.0614        | 3.0   | 1431 | 0.0724          | 0.9218    | 0.9317 | 0.9267 | 0.9797   |
| 0.0452        | 4.0   | 1908 | 0.0756          | 0.9283    | 0.9350 | 0.9316 | 0.9806   |
| 0.035         | 5.0   | 2385 | 0.0824          | 0.9221    | 0.9337 | 0.9279 | 0.9796   |
| 0.0263        | 6.0   | 2862 | 0.0895          | 0.9191    | 0.9319 | 0.9254 | 0.9787   |
| 0.02          | 7.0   | 3339 | 0.0991          | 0.9238    | 0.9335 | 0.9286 | 0.9789   |
| 0.0148        | 8.0   | 3816 | 0.1005          | 0.9277    | 0.9358 | 0.9317 | 0.9798   |
| 0.0124        | 9.0   | 4293 | 0.1014          | 0.9254    | 0.9356 | 0.9305 | 0.9801   |
| 0.01          | 10.0  | 4770 | 0.1048          | 0.9267    | 0.9354 | 0.9310 | 0.9796   |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3