HausaNLP/afrisenti-twi-regression

AfriSenti Twi Sentiment Regressor Description

Takes a text and predicts the sentiment value between -1 (Negative) to 1 (Positive) with 0 being Neutral.

Regression Value Description:

Value	Sentiment
-1	Negative
0	Neutral
1	Positive

How to Get Started with the Model

Use the code below to get started with the model.

import math
import torch
import pandas as pd
from transformers import AutoModelForSequenceClassification, AutoTokenizer

BATCH_SIZE = 32
ds = pd.read_csv('test.csv')
BASE_MODEL = 'HausaNLP/afrisenti-twi-regression'

device = 'cuda' if torch.cuda.is_available() else 'cpu'

tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
model = AutoModelForSequenceClassification.from_pretrained(BASE_MODEL)

nb_batches = math.ceil(len(ds)/BATCH_SIZE)
y_preds = []

for i in range(nb_batches):
  input_texts = ds[i * BATCH_SIZE: (i+1) * BATCH_SIZE]["tweet"]
  encoded = tokenizer(input_texts, truncation=True, padding="max_length", max_length=256, return_tensors="pt").to(device)
  y_preds += model(**encoded).logits.reshape(-1).tolist()

df = pd.DataFrame([ds['tweet'], ds['label'], y_preds], ["Text", "Label", "Prediction"]).T
df.to_csv('predictions.csv', index=False)