---
license: mit
datasets:
- mteb/tweet_sentiment_extraction
language:
- hi
- en
metrics:
- f1
- accuracy
pipeline_tag: text-classification
tags:
- hinglish
- sentiment
- sentiment analysis
widget:
- text: "tu mujhe pasandh heh"
  example_title: "Positive sentiment example 1"
- text: "❤️"
  example_title: "Positive sentiment example 1"
- text: "tu mujhe pasandh heh :( ;("
  example_title: "Negative sentiment"
- text: "aj mausam kesa heh?"
  example_title: "Neutral sentiment"
---
## Overview

The model is more optimized for hinglish + emojis and emojis seem to take more attention than the hinglish words.
This may be due to the base model being trained for emoji classification and then later trained for sentiment analysis.

This model is better if emojis are to be also included for sentiment analysis.
No Evaluation is done for data with only text and no emojis.

The model was fine-tuned with dataset: mteb/tweet_sentiment_extraction from huggingface
converted to hinglish text.

The model has a test loss of 0.6 and an f1 score of 0.74 on the unseen data from the dataset.