Update README.md

README.md

@@ -30,7 +30,7 @@ language:
 
 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
 
-- This model has been further trained from [BEE-spoke-data/bert-plus-L8-v1.0-allNLI_matryoshka](https://hf.co/BEE-spoke-data/bert-plus-L8-v1.0-allNLI_matryoshka) on 'v3.0' of the `synthetic text similarity' dataset.
+- This model has been further trained from [BEE-spoke-data/bert-plus-L8-v1.0-allNLI_matryoshka](https://hf.co/BEE-spoke-data/bert-plus-L8-v1.0-allNLI_matryoshka) on `v3.0` of the `synthetic text similarity` dataset.
 - Intended for use in comparing the cosine similarity of longer document embeddings and/or clustering them.
 - Matryoshka dims: [768, 512, 256, 128, 64]
 
@@ -58,36 +58,53 @@ print(embeddings)
 
 
 ## Usage (HuggingFace Transformers)
+
 Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
 
 ```python
-from transformers import AutoTokenizer, AutoModel
 import torch
+from transformers import AutoModel, AutoTokenizer
 
 
-#Mean Pooling - Take attention mask into account for correct averaging
+# 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()
-    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+    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
+    )
 
 
 # Sentences we want sentence embeddings for
-sentences = ['This is an example sentence', 'Each sentence is converted']
+sentences = ["This is an example sentence", "Each sentence is converted"]
 
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('BEE-spoke-data/bert-plus-L8-v1.0-synthSTSv3-4k')
-model = AutoModel.from_pretrained('BEE-spoke-data/bert-plus-L8-v1.0-synthSTSv3-4k')
+tokenizer = AutoTokenizer.from_pretrained(
+    "BEE-spoke-data/bert-plus-L8-v1.0-synthSTSv3-4k"
+)
+model = AutoModel.from_pretrained("BEE-spoke-data/bert-plus-L8-v1.0-synthSTSv3-4k")
 
 # Tokenize sentences
-encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+encoded_input = tokenizer(
+    sentences,
+    padding=True,
+    truncation=True,
+    return_tensors="pt",
+)
 
 # Compute token embeddings
 with torch.no_grad():
     model_output = model(**encoded_input)
 
 # Perform pooling. In this case, mean pooling.
-sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+sentence_embeddings = mean_pooling(
+    model_output,
+    encoded_input["attention_mask"],
+)
 
 print("Sentence embeddings:")
 print(sentence_embeddings)