YAML Metadata
Warning:
empty or missing yaml metadata in repo card
(https://huggingface.co/docs/hub/model-cards#model-card-metadata)
Nepali Covid Tweet Classification
This model was developed by finetuning the NepaliBERT model previously developed by me on Nepali COVID-related tweets. This dataset has about 15000 observations annotated with positive, negative, and neutral labels. NepaliBERT model was able to achieve SOTA results while finetuning this model for text classification. While training the model, the evaluation metrics obtained were:
- Training loss: 0.35592623149202174
- Validation loss: 0.6570735214928906
- F1 Score (Weighted): 0.7719232825307907
LABELS INDICATOR
- LABEL 0 - Neutral
- LABEL 1 - Positive
- Label 2 - Negative
USAGE
from transformers import pipeline
classifier = pipeline("text-classification", model = "Shushant/NepaliCovidTweetsClassification")
classifier("आउँदा केही दिनमा अमेरिकाले १५ लाखभन्दा बढी नेपालीलाई पुग्नेगरी कोभीड१९ खोप निशुल्क उपलब्ध गराउंदैछ।")
- Downloads last month
- 4
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.