Update README.md

README.md

@@ -0,0 +1,29 @@
+```python
+import torch
+from scipy.spatial.distance import cosine
+
+from transformers import AutoModel, AutoTokenizer
+
+# Load the model
+tokenizer = AutoTokenizer.from_pretrained("johngiorgi/declutr-small")
+model = AutoModel.from_pretrained("johngiorgi/declutr-small")
+
+# Prepare some text to embed
+text = [
+    "A smiling costumed woman is holding an umbrella.",
+    "A happy woman in a fairy costume holds an umbrella.",
+]
+inputs = tokenizer(text, padding=True, truncation=True, return_tensors="pt")
+
+# Embed the text
+with torch.no_grad():
+    sequence_output, _ = model(**inputs, output_hidden_states=False)
+
+# Mean pool the token-level embeddings to get sentence-level embeddings
+embeddings = torch.sum(
+    sequence_output * inputs["attention_mask"].unsqueeze(-1), dim=1
+) / torch.clamp(torch.sum(inputs["attention_mask"], dim=1, keepdims=True), min=1e-9)
+
+# Compute a semantic similarity via the cosine distance
+semantic_sim = 1 - cosine(embeddings[0], embeddings[1])
+```