README.md

metadata

pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
license: mit
datasets:
  - sentence-transformers/embedding-training-data
  - clips/mfaq
  - squad
  - eli5
language:
  - da
library_name: sentence-transformers

Work in progress

MiniLM-L6-danish-encoder

This is a lightweight (~22 M parameters) sentence-transformers 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.

The maximum sequence length is 128 tokens.

The model was not pre-trained from scratch but adapted from the English version with a tokenizer trained on Danish text.

When using the model to retrieve relevant passages for a given query - "Query: " should be added to the query:

query = "Kan man cykle på en vej?"
query_template = f"Query: {query}"

#query_template kan now be embedded and similarity compared to other passages

Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:

pip install -U sentence-transformers

Then you can use the model like this:

from sentence_transformers import SentenceTransformer
sentences = ["Query: Kører der cykler på vejen?", "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, 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.

from transformers import AutoTokenizer, AutoModel
import torch
import torch.nn.functional as F

#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 = ["Query: Kører der cykler på vejen?", "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)