HausaNLP/

afrisenti-pcm-regression

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)