Update README.md

README.md

@@ -15,36 +15,68 @@ library_name: sentence-transformers
 
 # MiniLM-L6-danish-encoder 
 
-This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for tasks like clustering or semantic search. Maximum sequence length is 256 tokens.
+This is a lightweight (~22 M parameters) [sentence-transformers](https://www.SBERT.net) model for Danish NLP: It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for tasks like clustering or semantic search. 
 
-## Usage (Sentence-Transformers)
+The maximum sequence length is 256 tokens.
+
+The model was not pre-trained from scratch but adapted from the English version with a [tokenizer](https://huggingface.co/KennethTM/bert-base-uncased-danish) trained on Danish text.
+
+# Usage (Sentence-Transformers)
 
 Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
 
 ```
 pip install -U sentence-transformers
 ```
-
 Then you can use the model like this:
 
 ```python
 from sentence_transformers import SentenceTransformer
-sentences = ["This is an example sentence", "Each sentence is converted"]
+sentences = ["En mand løber på vejen.", "En panda løber på vejen.", "En mand kører hurtigt forbi på cykel."]
 
 model = SentenceTransformer('KennethTM/MiniLM-L6-danish-encoder')
 embeddings = model.encode(sentences)
 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.
 
-## Evaluation Results
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+import torch.nn.functional as F
 
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
+#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)
 
-## Full Model Architecture
-```
-SentenceTransformer(
-  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
-  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
-  (2): Normalize()
-)
+# Sentences we want sentence embeddings for
+sentences = ["En mand løber på vejen.", "En panda løber på vejen.", "En mand kører hurtigt forbi på cykel."]
+
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained('KennethTM/MiniLM-L6-danish-encoder')
+model = AutoModel.from_pretrained('KennethTM/MiniLM-L6-danish-encoder')
+
+# 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
+sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+
+# Normalize embeddings
+sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
+
+print("Sentence embeddings:")
+print(sentence_embeddings)
 ```
+
+# Evaluation
+
+The performance of the pretrained model was evaluated using [ScandEval](https://github.com/ScandEval/ScandEval).
+