Update README.md

README.md

@@ -34,22 +34,19 @@ import torch
 
 from transformers import AutoModel, AutoTokenizer
 
-
-
 ### German example
 
-def generate_sentence_embedding(sentence, model_name="jgrosjean-mathesis/swissbert-for-sentence-embeddings"):
-    # Load swissBERT model
-    model = AutoModel.from_pretrained(model_name)
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    model.set_default_language("de_CH")
+# Load swissBERT for sentence embeddings model
+model_name="jgrosjean-mathesis/swissbert-for-sentence-embeddings"
+model = AutoModel.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model.set_default_language("de_CH")
+
+def generate_sentence_embedding(sentence, ):
 
     # Tokenize input sentence
     inputs = tokenizer(sentence, padding=True, truncation=True, return_tensors="pt", max_length=512)
 
-    # Set the model to evaluation mode
-    model.eval()
-
     # Take tokenized input and pass it through the model
     with torch.no_grad():
         outputs = model(**inputs)
@@ -74,7 +71,6 @@ tensor([[ 5.6306e-02, -2.8375e-01, -4.1495e-02,  7.4393e-02, -3.1552e-01,
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 This model has been trained on news articles only. Hence, it might not perform as well on other text classes.
-This multilingual model has not been fine-tuned for cross-lingual transfer. It is intended for computing sentence embeddings that can be compared mono-lingually.
 
 ## Training Details