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README.md

@@ -5,7 +5,7 @@ tags:
   - generated_from_trainer
   - transformers
 library_name: sentence-transformers
-pipeline_tag: text-ranking
+pipeline_tag: sentence-similarity
 model-index:
   - name: bge_reranker
     results: []
@@ -13,9 +13,9 @@ inference:
   parameters:
     normalize: True
 widget:
-  - input:
-      query: "Hello, world!"
-      documents:
+  - inputs:
+      source_sentence: "Hello, world!"
+      sentences:
         - "Hello! How are you?"
         - "Cats and dogs"
         - "The sky is blue"
@@ -76,6 +76,18 @@ curl "https://xxxxxxx.us-east-1.aws.endpoints.huggingface.cloud" \
 
 ```python
 from FlagEmbedding import FlagReranker
+
+class RerankRequest(BaseModel):
+    query: str
+    documents: list[str]
+
+# Prepare array
+arr = []
+for element in request.documents:
+    arr.append([request.query, element])
+print(arr)
+
+# Inference
 reranker = FlagReranker('netandreus/bge-reranker-v2-m3', use_fp16=True)
 scores = reranker.compute_score(arr, normalize=True)
 if not isinstance(scores, list):

handler.py

@@ -19,22 +19,22 @@ class EndpointHandler:
         Expected input format:
         {
             "inputs": {
-                "query": "Your query here",
-                "documents": ["Document 1", "Document 2", ...]
+                "source_sentence": "Your query here",
+                "sentences": ["Document 1", "Document 2", ...]
             },
             "normalize": true  # Optional; defaults to False
         }
         """
         inputs = data.get("inputs", {})
-        query = inputs.get("query")
-        documents = inputs.get("documents", [])
+        source_sentence = inputs.get("source_sentence")
+        sentences = inputs.get("sentences", [])
         normalize = data.get("normalize", False)
 
-        if not query or not documents:
-            return [{"error": "Both 'query' and 'documents' fields are required inside 'inputs'."}]
+        if not source_sentence or not sentences:
+            return [{"error": "Both 'source_sentence' and 'sentences' fields are required inside 'inputs'."}]
 
         # Prepare input pairs
-        pairs = [[query, text] for text in documents]
+        pairs = [[source_sentence, text] for text in sentences]
 
         # Tokenize input pairs
         tokenizer_inputs = self.tokenizer(