Naija Twitter Sentiment Afriberta Large

🚀 naija-twitter-sentiment-afriberta-large

naija-twitter-sentiment-afriberta-large is the first multilingual Twitter sentiment classification model for four Nigerian languages (Hausa, Igbo, Nigerian Pidgin, and Yorùbá). It is based on a fine - tuned castorini/afriberta_large large model, offering state - of - the - art performance for the Twitter sentiment classification task.

✨ Features

• Supports four Nigerian languages: Hausa, Igbo, Nigerian Pidgin, and Yorùbá.
• Achieves state - of - the - art performance on the Twitter sentiment classification task.
• Classifies tweets into three sentiment classes: negative, neutral, and positive.

📦 Installation

The README doesn't provide specific installation steps, so this section is skipped.

💻 Usage Examples

Basic Usage

from transformers import AutoModelForSequenceClassification
from transformers import AutoTokenizer
import numpy as np
from scipy.special import softmax

MODEL = "Davlan/naija-twitter-sentiment-afriberta-large"
tokenizer = AutoTokenizer.from_pretrained(MODEL)

# PT
model = AutoModelForSequenceClassification.from_pretrained(MODEL)

text = "I like you"
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
scores = output[0][0].detach().numpy()
scores = softmax(scores)

id2label = {0:"positive", 1:"neutral", 2:"negative"}

ranking = np.argsort(scores)
ranking = ranking[::-1]
for i in range(scores.shape[0]):
    l = id2label[ranking[i]]
    s = scores[ranking[i]]
    print(f"{i+1}) {l} {np.round(float(s), 4)}")

📚 Documentation

Intended uses & limitations

How to use

You can use this model with Transformers for Sentiment Classification. The above code example shows the basic usage.

Limitations and bias

This model is limited by its training dataset and domain (Twitter). It may not generalize well for all use cases in different domains.

Training procedure

This model was trained on a single Nvidia RTX 2080 GPU with recommended hyperparameters from the original NaijaSenti paper.

Eval results on Test set (F - score), average over 5 runs.

BibTeX entry and citation info

@inproceedings{Muhammad2022NaijaSentiAN,
  title={NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis},
  author={Shamsuddeen Hassan Muhammad and David Ifeoluwa Adelani and Sebastian Ruder and Ibrahim Said Ahmad and Idris Abdulmumin and Bello Shehu Bello and Monojit Choudhury and Chris C. Emezue and Saheed Salahudeen Abdullahi and Anuoluwapo Aremu and Alipio Jeorge and Pavel B. Brazdil},
  year={2022}
}