afrisenti-pcm-regression

AfriSenti Nigerian Pidgin Sentiment Regressor Description

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

Regression Value Description:

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-pcm-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)