Medical Embedded V2

🚀 sentence-transformers/distiluse-base-multilingual-cased-v1

This model from sentence-transformers maps sentences and paragraphs to a 512-dimensional dense vector space. It can be used for tasks such as clustering or semantic search.

🚀 Quick Start

📦 Installation

Using this model becomes easy when you have sentence-transformers installed. You can install it with the following command:

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage

After installation, you can use the model like this:

from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('sentence-transformers/distiluse-base-multilingual-cased-v1')
embeddings = model.encode(sentences)
print(embeddings)

🔧 Technical Details

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
  (2): Dense({'in_features': 768, 'out_features': 512, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)

📄 License

This model is licensed under the apache-2.0 license.

📚 Documentation

Citing & Authors

This model was trained by sentence-transformers.

If you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "http://arxiv.org/abs/1908.10084",
}

Additional Information