update README

README.txt

@@ -11,15 +11,50 @@ Finnish Sentence BERT trained from FinBERT
 
 ## Usage
 
-Please refer to the [HuggingFace documentation] (https://huggingface.co/sentence-transformers/bert-base-nli-mean-tokens)
+The same as in [HuggingFace documentation] (https://huggingface.co/sentence-transformers/bert-base-nli-mean-tokens). Either through `SentenceTransformer` or `HuggingFace Transformers`
 
-Briefly, using the `SentenceTransformer` library,
+### SentenceTransformer
 
 ```
 from sentence_transformers import SentenceTransformer
 sentences = ["Tämä on esimerkkilause.", "Tämä on toinen lause."]
 
-model = SentenceTransformer('sbert-cased-finnish-paraphrase')
+model = SentenceTransformer('TurkuNLP/sbert-cased-finnish-paraphrase')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
+
+### HuggingFace Transformers
+
+```
+from transformers import AutoTokenizer, AutoModel
+import torch
+
+
+#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)
+
+
+# Sentences we want sentence embeddings for
+sentences = ["Tämä on esimerkkilause.", "Tämä on toinen lause."]
+
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained('TurkuNLP/sbert-cased-finnish-paraphrase')
+model = AutoModel.from_pretrained('TurkuNLP/sbert-cased-finnish-paraphrase')
+
+# Tokenize sentences
+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'])
+
+print("Sentence embeddings:")
+print(sentence_embeddings)
+```