Upload sentenceTranformer.py

sentenceTranformer.py

@@ -0,0 +1,31 @@
+from transformers import Pipeline
+import torch.nn.functional as F
+import torch
+
+# copied from the model card
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+
+
+class SentenceEmbeddingPipeline(Pipeline):
+    def _sanitize_parameters(self, **kwargs):
+        # we don't have any hyperameters to sanitize
+        preprocess_kwargs = {}
+        return preprocess_kwargs, {}, {}
+
+    def preprocess(self, inputs):
+        encoded_inputs = self.tokenizer(inputs, padding=True, truncation=True, return_tensors='pt')
+        return encoded_inputs
+
+    def _forward(self, model_inputs):
+        outputs = self.model(**model_inputs)
+        return {"outputs": outputs, "attention_mask": model_inputs["attention_mask"]}
+
+    def postprocess(self, model_outputs):
+        # Perform pooling
+        sentence_embeddings = mean_pooling(model_outputs["outputs"], model_outputs['attention_mask'])
+        # Normalize embeddings
+        sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
+        return sentence_embeddings