---
language:
- mn
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
base_model: cardiffnlp/twitter-roberta-base-sentiment
model-index:
- name: mongolian-twitter-roberta-base-sentiment-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-twitter-roberta-base-sentiment-ner

This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1674
- Precision: 0.7560
- Recall: 0.8395
- F1: 0.7955
- Accuracy: 0.9540

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4091        | 1.0   | 477  | 0.2507          | 0.5166    | 0.6789 | 0.5868 | 0.9162   |
| 0.2467        | 2.0   | 954  | 0.2363          | 0.6415    | 0.7465 | 0.6900 | 0.9243   |
| 0.2051        | 3.0   | 1431 | 0.1921          | 0.6732    | 0.7857 | 0.7251 | 0.9374   |
| 0.1738        | 4.0   | 1908 | 0.1746          | 0.6965    | 0.8038 | 0.7463 | 0.9440   |
| 0.1475        | 5.0   | 2385 | 0.1680          | 0.7217    | 0.8172 | 0.7665 | 0.9472   |
| 0.1305        | 6.0   | 2862 | 0.1736          | 0.7209    | 0.8228 | 0.7685 | 0.9483   |
| 0.1116        | 7.0   | 3339 | 0.1621          | 0.7337    | 0.8296 | 0.7787 | 0.9518   |
| 0.099         | 8.0   | 3816 | 0.1684          | 0.7353    | 0.8318 | 0.7806 | 0.9508   |
| 0.0882        | 9.0   | 4293 | 0.1666          | 0.7625    | 0.8417 | 0.8002 | 0.9547   |
| 0.0799        | 10.0  | 4770 | 0.1674          | 0.7560    | 0.8395 | 0.7955 | 0.9540   |


### Framework versions

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