Create app.py

app.py

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+import streamlit as st
+
+# List of sentences
+sentence1 = [f"U.S. President Barack Obama declared that the U.S. will refrain from deploying troops in Ukraine."]
+sentence2 = [f"Joe Biden said we’d not send U.S. troops to fight Russian troops in Ukraine, but we would provide robust military assistance and try to unify the Western world against Russia’s aggression."]
+# Create a dropdown menu
+selected_sentence1 = st.selectbox("Select first sentence:", sentence1)
+selected_sentence2 = st.selectbox("Select first sentence:", sentence2)
+
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+
+model_name = "MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+
+premise = sentence1
+hypothesis = sentence2
+input = tokenizer(premise, hypothesis, truncation=True, return_tensors="pt")
+output = model(input["input_ids"].to(device))  # device = "cuda:0" or "cpu"
+prediction = torch.softmax(output["logits"][0], -1).tolist()
+label_names = ["support", "neutral", "refute"]
+prediction = {name: round(float(pred) * 100, 1) for pred, name in zip(prediction, label_names)}
+print(prediction)
+
+st.write("Result:", prediction)