HausaNLP
/

afrisenti-yor-regression

@@ -3,197 +3,46 @@ library_name: transformers
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 tags: []
 ---
+## AfriSenti Yoruba 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-yor-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)
+```