handsker-pretrained-300d

This is a GloVe model trained on the entirety of Danish Dynaword for 10 epochs converted into a sentence-transformers model with mean pooling. The model was trained using the GlovPy Python library. The model uses a Bert-style tokenizer that was also trained on Dynaword.

Model Details

Model Description

• Model Type: Sentence Transformer
• Maximum Sequence Length: 1000000 tokens
• Output Dimensionality: 300 dimensions
• Similarity Function: Cosine Similarity
• Supported Modality: Text

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): WordEmbeddings({'tokenizer_class': 'sentence_transformers.sentence_transformer.modules.tokenizer.whitespace.WhitespaceTokenizer', 'update_embeddings': False, 'max_seq_length': 1000000})
  (1): Pooling({'embedding_dimension': 300, 'pooling_mode': 'mean', 'include_prompt': True})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("kardosdrur/handsker-pretrained-300d")
# Run inference
sentences = [
    'The weather is lovely today.',
    "It's so sunny outside!",
    'He drove to the stadium.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 300]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.6504, 0.7633],
#         [0.6504, 1.0000, 0.5803],
#         [0.7633, 0.5803, 1.0000]])

Training Details

Framework Versions

• Python: 3.14.7
• Sentence Transformers: 6.0.1
• Transformers: 5.16.1
• PyTorch: 2.14.0+cu130
• Accelerate:
• Datasets: 5.0.1
• Tokenizers: 0.23.2

Additional Resources

• Training and Finetuning Embedding Models with Sentence Transformers: the end-to-end guide for training or finetuning Sentence Transformer models.
• Introduction to Matryoshka Embedding Models: variable-size embeddings that can be truncated with minimal quality loss.
• Binary and Scalar Embedding Quantization for Significantly Faster & Cheaper Retrieval: post-training compression of embedding vectors.
• Multimodal Embedding & Reranker Models with Sentence Transformers: use text, image, audio, and video models through the same API.
• Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers: train multimodal embedding models, with a Visual Document Retrieval walkthrough.

Citation

BibTeX