metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dense
base_model: cambridgeltl/SapBERT-UMLS-2020AB-all-lang-from-XLMR
pipeline_tag: sentence-similarity
library_name: sentence-transformers
language:
  - en
  - zh
  - de
  - es
  - ko
  - ja
  - th
  - fi
  - ru
  - tr

SentenceTransformer based on cambridgeltl/SapBERT-UMLS-2020AB-all-lang-from-XLMR

This is a sentence-transformers model finetuned from cambridgeltl/SapBERT-UMLS-2020AB-all-lang-from-XLMR. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: cambridgeltl/SapBERT-UMLS-2020AB-all-lang-from-XLMR
Maximum Sequence Length: 25 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity

Model Sources

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

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 25, 'do_lower_case': False, 'architecture': 'XLMRobertaModel'})
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, '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("sentence_transformers_model_id")
# 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, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0078, 0.6875, 0.3496],
#         [0.6875, 0.9961, 0.3691],
#         [0.3496, 0.3691, 1.0000]], dtype=torch.bfloat16)

Training Details

Framework Versions

Python: 3.12.3
Sentence Transformers: 5.0.0
Transformers: 4.48.0
PyTorch: 2.5.0+cu121
Accelerate: 1.8.1
Datasets: 3.6.0
Tokenizers: 0.21.2

UMCU
/

SapBERT-UMLS-2020AB-all-lang-from-XLMR-ST_bf16