pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
license: mit
datasets:
- squad
- eli5
- sentence-transformers/embedding-training-data
language:
- da
library_name: sentence-transformers
New version available, trained on more data and otherwise identical KennethTM/MiniLM-L6-danish-encoder-v2
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 512 tokens.
The model was not pre-trained from scratch but adapted from the English version of sentence-transformers/all-MiniLM-L6-v2 with a Danish tokenizer.
Trained on ELI5 and SQUAD data machine translated from English to Danish.
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 = ["Kører der cykler 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 = ["Kører der cykler 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)