align model_card_templates.py with code (#3081)

See:
https://github.com/UKPLab/sentence-transformers/blob/348190d46b0c010c7a4693f198f0ddf70c6ceb35/sentence_transformers/models/Pooling.py#L163
https://github.com/UKPLab/sentence-transformers/blob/348190d46b0c010c7a4693f198f0ddf70c6ceb35/sentence_transformers/models/Pooling.py#L170

sentence_transformers/model_card_templates.py

@@ -130,7 +130,7 @@ def model_card_get_pooling_function(pooling_mode):
 # Max Pooling - Take the max value over time for every dimension.
 def max_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()
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).to(token_embeddings.dtype)
     token_embeddings[input_mask_expanded == 0] = -1e9  # Set padding tokens to large negative value
     return torch.max(token_embeddings, 1)[0]
 """,
@@ -142,7 +142,7 @@ def max_pooling(model_output, attention_mask):
 #Mean Pooling - Take attention mask into account for correct averaging
 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()
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).to(token_embeddings.dtype)
     return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
 """,
             )