---
language:
- mn
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: mongolian-roberta-large-mnli-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-roberta-large-mnli-ner

This model is a fine-tuned version of [roberta-large-mnli](https://huggingface.co/roberta-large-mnli) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1941
- Precision: 0.7734
- Recall: 0.8488
- F1: 0.8094
- Accuracy: 0.9582

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3433        | 1.0   | 477  | 0.2252          | 0.6196    | 0.7338 | 0.6719 | 0.9288   |
| 0.2067        | 2.0   | 954  | 0.1859          | 0.6981    | 0.7908 | 0.7416 | 0.9381   |
| 0.165         | 3.0   | 1431 | 0.1776          | 0.7308    | 0.8112 | 0.7689 | 0.9455   |
| 0.1362        | 4.0   | 1908 | 0.1639          | 0.7513    | 0.8265 | 0.7871 | 0.9520   |
| 0.109         | 5.0   | 2385 | 0.1703          | 0.7524    | 0.8302 | 0.7894 | 0.9517   |
| 0.0873        | 6.0   | 2862 | 0.1690          | 0.7643    | 0.8396 | 0.8002 | 0.9552   |
| 0.0697        | 7.0   | 3339 | 0.1754          | 0.7696    | 0.8442 | 0.8052 | 0.9557   |
| 0.0552        | 8.0   | 3816 | 0.1793          | 0.7687    | 0.8468 | 0.8059 | 0.9572   |
| 0.0434        | 9.0   | 4293 | 0.1878          | 0.7842    | 0.8507 | 0.8161 | 0.9580   |
| 0.0354        | 10.0  | 4770 | 0.1941          | 0.7734    | 0.8488 | 0.8094 | 0.9582   |


### Framework versions

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