Feature Extraction
Transformers
PyTorch
English
contrastive_clinical
contrastive-learning
clinical-text
medical-nlp
entity-anonymization
triplet-loss
clinical-modernbert
sentence-embeddings
custom_code
Eval Results (legacy)
Instructions to use nikhil061307/contrastive-learning-bert-added-token-v5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nikhil061307/contrastive-learning-bert-added-token-v5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="nikhil061307/contrastive-learning-bert-added-token-v5", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nikhil061307/contrastive-learning-bert-added-token-v5", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#2
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:d8a6dc334c725a407246224f4df53081653d05f5d62cae38ad63f27f017f0da1
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size 546361784
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