add generic pipeline

README.md

@@ -12,6 +12,7 @@ language:
 metrics:
 - spearmanr
 pipeline_tag: sentence-similarity
+library_name: generic
 ---
 
 # sup-simcse-ja-base

pipeline.py

@@ -0,0 +1,34 @@
+# copy from https://github.com/huggingface/api-inference-community/blob/main/docker_images/sentence_transformers/app/pipelines/sentence_similarity.py
+
+import os
+from typing import Dict, List, Union
+
+from sentence_transformers import SentenceTransformer, util
+
+
+class PreTrainedPipeline:
+    def __init__(
+        self,
+        model_id: str,
+    ):
+        self.model = SentenceTransformer(
+            model_id, use_auth_token=os.getenv("HF_API_TOKEN")
+        )
+
+    def __call__(self, inputs: Dict[str, Union[str, List[str]]]) -> List[float]:
+        """
+        Args:
+            inputs (:obj:`dict`):
+                a dictionary containing two keys, 'source_sentence' mapping
+                to the sentence that will be compared against all the others,
+                and 'sentences', mapping to a list of strings to which the
+                source will be compared.
+        Return:
+            A :obj:`list` of floats: Cosine similarity between `source_sentence` and each sentence from `sentences`.
+        """
+        embeddings1 = self.model.encode(
+            inputs["source_sentence"], convert_to_tensor=True
+        )
+        embeddings2 = self.model.encode(inputs["sentences"], convert_to_tensor=True)
+        similarities = util.pytorch_cos_sim(embeddings1, embeddings2).tolist()[0]
+        return similarities

requirements.txt

@@ -0,0 +1,2 @@
+sentence-transformers
+fugashi[unidic-lite]