Update README.md

README.md

@@ -1,3 +1,42 @@
----
-license: mit
----
+---
+license: mit
+---
+this is the placeholder model card for the (finess-benchmark-space)[https://huggingface.co/spaces/enzoescipy/finesse-benchmark-space] and its (database)[https://huggingface.co/datasets/enzoescipy/finesse-benchmark-results].
+
+```python
+import torch
+from typing import List
+from transformers import AutoConfig, PreTrainedModel  # Optional: for loading configs
+
+from finesse_benchmark.interfaces import  FinesseSynthesizer
+
+# --- Custom Embedder Example ---
+# Uncomment and customize this class for your embedder.
+
+class AverageSynthesizer(FinesseSynthesizer):
+    """
+    Average Synthesizer: Computes the mean of input embeddings without using any model.
+    """
+    def __init__(self, config_path: str):
+        super().__init__()
+        # No model to load for average pooling
+        print(f"{self.__class__.__name__} initialized - Average pooling ready.")
+
+    def synthesize(self, embeddings: torch.Tensor, **kwargs) -> torch.Tensor:
+        """
+        Average synthesis: Compute the mean along the sequence dimension.
+        
+        Args:
+            embeddings: torch.Tensor of shape (batch, seq_len, embedding_dim)
+            **kwargs: Additional arguments
+        
+        Returns:
+            torch.Tensor of shape (batch, embedding_dim)
+        """
+        return embeddings.mean(dim=1)
+
+    def device(self):
+        return "cpu"
+```
+
+it just averages the embedding vectors.