Test.

README.md

@@ -1,19 +1,22 @@
 ---
 tags:
 - sentence-transformers
+- feature-extraction
 ---
 
 # TODO: Name of Model
 
 TODO: Description
 
-## Model Description
+## Model Description
 TODO: Add relevant content
 
 (0) Base Transformer Type: RobertaModel
+
 (1) Pooling mean
 
-## Usage (Sentence-Transformers)
+
+## Usage (Sentence-Transformers)
 
 Using this model becomes more convenient when you have [sentence-transformers](https://github.com/UKPLab/sentence-transformers) installed:
 
@@ -31,7 +34,7 @@ print(embeddings)
 ```
 
 
-## Usage (HuggingFace Transformers)
+## Usage (HuggingFace Transformers)
 
 ```
 from transformers import AutoTokenizer, AutoModel
@@ -46,17 +49,17 @@ def max_pooling(model_output, attention_mask):
     max_over_time = torch.max(token_embeddings, 1)[0]
     return max_over_time
 
-#Sentences we want sentence embeddings for
+# Sentences we want sentence embeddings for
 sentences = ['This is an example sentence']
 
-#Load model from HuggingFace Hub
+# Load model from HuggingFace Hub
 tokenizer = AutoTokenizer.from_pretrained(TODO)
 model = AutoModel.from_pretrained(TODO
 
-#Tokenize sentences
+# Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, max_length=128, return_tensors='pt'))
 
-#Compute token embeddings
+# Compute token embeddings
 with torch.no_grad():
     model_output = model(**encoded_input)
 
@@ -69,8 +72,8 @@ print(sentence_embeddings)
 
 
 
-## TODO: Training Procedure
+## TODO: Training Procedure
 
-## TODO: Evaluation Results
+## TODO: Evaluation Results
 
-## TODO: Citing & Authors
+## TODO: Citing & Authors

pytorch_model.bin

@@ -1,3 +1,3 @@
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