Upload inference_chatrag.py

inference_chatrag.py

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+import pandas as pd
+import torch
+import numpy as np
+
+from transformers import AutoModelForSequenceClassification
+from transformers import AutoTokenizer
+
+model = AutoModelForSequenceClassification.from_pretrained("deberta-classification-chatrag/checkpoint-6342")
+tokenizer = AutoTokenizer.from_pretrained("deberta-classification-chatrag/checkpoint-6342")
+
+
+result = ["Comment puis-je renouveler un passeport ?", "Combien font deux et deux ?", "Écris un début de lettre de recommandation pour la Dinum"]
+
+result = pd.DataFrame(result, columns=['query'])
+
+complete_probabilities = []
+
+for text in result["query"].tolist():
+  encoding = tokenizer(text, return_tensors="pt")
+  encoding = {k: v.to(model.device) for k,v in encoding.items()}
+
+  outputs = model(**encoding)
+
+  logits = outputs.logits
+  logits.shape
+
+  # apply sigmoid + threshold
+  sigmoid = torch.nn.Sigmoid()
+  probs = sigmoid(logits.squeeze().cpu())
+  predictions = np.zeros(probs.shape)
+
+  # Extract the float value from the tensor
+  float_value = probs.item()
+
+  complete_probabilities.append(float_value)
+
+result["prob"] = complete_probabilities
+
+print(result)