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---
library_name: transformers
tags: []
---
# Model Card for Model ID
This model was developed by finetuning the [DistilBERT Nepali Model](https://huggingface.co/Sakonii/distilbert-base-nepali). The model classifies the Nepali tweets related to COVID19 into three categories: neutral, positive and negative.
- **Developed by:** Jeevan
- **Model type:** DistilBERT Nepali
- **Language(s) (NLP):** Nepali
- **Finetuned from model [optional]:** [DistilBERT Nepali Model](https://huggingface.co/Sakonii/distilbert-base-nepali)
## Training Details
### Training Data
The dataset used for finetuning this model can be found at [NepCOV19Tweets](https://www.kaggle.com/datasets/mathew11111/nepcov19tweets) which contains Nepali tweets related to COVID-19.
### Training HyperParameters
* Batch size: 16
* Learning Rate: 0.0001
* Optimizer: AdamW
* Epochs: 10
## Evaluation
* Training loss: 0.2414
* Precision: 0.73
* Recall: 0.73
* F1 Score (Weighted): 0.73
## Labels
* Neutral: 0
* Positive: 1
* Negative: 2
## USAGE
```python
from transformers import pipeline
pipe = pipeline("text-classification", model="xap/Sentiment_Analysis_NepaliCovidTweets")
pipe("अमेरिकामा कोभिड बाट एकै दिन चार हजारभन्दा बढीको मृत्यु")
```
## Citation
```
@misc {jeevan_2024,
author = { {jeevan} },
title = { Sentiment_Analysis_NepaliCovidTweets (Revision 3086409) },
year = 2024,
url = { https://huggingface.co/xap/Sentiment_Analysis_NepaliCovidTweets },
doi = { 10.57967/hf/2243 },
publisher = { Hugging Face }
}
```
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