Feature Extraction
Transformers
Safetensors
OpenVINO
PyTorch
English
biomedical
bert
embeddings
benchmark
sentence-similarity
intel-xeon
cpu-inference
quantization
event-separation
clinical-nlp
Instructions to use Dotsin/lbm-benchmarking-embeddingsFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dotsin/lbm-benchmarking-embeddingsFT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Dotsin/lbm-benchmarking-embeddingsFT")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Dotsin/lbm-benchmarking-embeddingsFT", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Access to the Biomedical Causal-Similarity Embedding Layer
These models are released by Dotsin.ai as the open biomedical sentence-embedding
layer that fronts a secure data hub. To download the weights you must sign in with a
Hugging Face account and accept the terms below. Access is granted automatically once the
form is submitted; we keep a record of who has accepted.
By requesting access you confirm that:
- You will not use these weights to identify, profile, or re-identify individuals from
biomedical or behavioural text. - You will not redistribute the weights outside Hugging Face without preserving this
gated-access requirement. - You will cite this repository if the weights or benchmark suite contribute to a
publication. - The weights are provided under Apache-2.0 (see LICENSE) with no warranty of any kind.
Log in or Sign Up to review the conditions and access this model content.
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