Create README.md

README.md

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+import torch
+from torch.utils.data import Dataset, DataLoader
+from torch.optim import AdamW
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch.nn.functional as F
+
+MODEL = "AventIQ-AI/sentiment-analysis-for-user-reviews-sentiment"
+tokenizer = AutoTokenizer.from_pretrained(MODEL)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL,num_labels=2,ignore_mismatched_sizes=True)
+
+
+def predict(text):
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128)
+    with torch.no_grad():
+        outputs = quantize_model(**inputs)
+        probs = F.softmax(outputs.logits, dim=1)
+        pred = torch.argmax(probs, dim=1).item()
+        label_map = {0: "Negative", 1: "Neutral", 2: "Positive"}
+    return f"Sentiment: {label_map[pred]} (Confidence: {probs[0][pred]:.2f})"
+
+# Test predictions
+print("\nTest Predictions:")
+print(predict("the product quality is just so so"))