license: gpl-3.0 language:
- en base_model:
- cambridgeltl/SapBERT-UMLS-2020AB-all-lang-from-XLMR pipeline_tag: sentence-similarity library_name: sentence-transformers tags:
- medical
TLDR; Re-formatted version of SapBERT-UMLS-2020AB-all-lang-from-XLMR, so that it can be used directly with Sentence-transformers.
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([[1.0000, 0.6878, 0.3458],
# [0.6878, 1.0000, 0.3685],
# [0.3458, 0.3685, 1.0000]])
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