first test

app.py

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+import evaluate
+from evaluate.utils import launch_gradio_widget
+
+
+module = evaluate.load("ahnyeonchan/cosine_sim_btw_embeddings_of_same_semantics")
+
+launch_gradio_widget(module)

cossim.py

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+import datasets
+import evaluate
+from typing import List, Union
+import torch
+
+import torch.nn.functional as F
+import torch.nn as nn
+_DESCRIPTION = """
+Cosine similarity between two pairs of embeddings where each embedding represents the semantics of object .
+
+"""
+
+_KWARGS_DESCRIPTION = """
+Args:
+    predictions (`list` of a list of `int`): a group of embeddings
+    references (`list` of `int`): the other group of embeddings paired with the predictions
+    
+Returns:
+    cos_similarity ("float") : average cosine similarity between two pairs of embeddings
+    
+Examples:
+
+    Example 1-A simple example
+        >>> cos_similarity_metrics = evaluate.load("ahnyeonchan/cosine_sim_btw_embeddings_of_same_semantics")
+        >>> results = accuracy_metric.compute(references=[[1.0, 1.0], [0.0, 1.0]], predictions=[[1.0, 1.0], [0.0, 1.0]])
+        >>> print(results)
+        {'cos_similarity': 1.0}
+"""
+
+_CITATION = """"""
+
+@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
+class CosSim(evaluate.Metric):
+    def __init__(self, *args, **kwargs):
+        super(CosSim, self).__init__(*args, **kwargs)
+        self.cossim = nn.CosineSimilarity()
+        
+    def _info(self):
+        return evaluate.MetricInfo(
+            description=_DESCRIPTION,
+            citation=_CITATION,
+            inputs_description=_KWARGS_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "predictions": datasets.Sequence(datasets.Value("float32")),
+                    "references": datasets.Sequence(datasets.Value("float32")),
+                }
+            ),
+            reference_urls=[],
+        )
+
+    def _compute(self, predictions: List[List], references: List[List]):
+        
+        if isinstance(predictions, torch.Tensor):
+            predictions = torch.Tensor(predictions)
+        elif isinstance(predictions, list):
+            predictions = torch.Tensor(predictions)
+        else:
+            raise NotImplementedError()
+        
+        if isinstance(references, torch.Tensor):
+            references = torch.Tensor(references)
+        elif isinstance(references, list):
+            references = torch.Tensor(references)
+        else:
+            raise NotImplementedError()
+        
+        cosims = self.cossim(predictions, references)
+        val = torch.mean(cossim).item()
+        
+        return {
+            "cos_similarity": float(val)
+        }

requirements.txt

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+torch