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
This is an automated PR created with https://huggingface.co/spaces/safetensors/convert
This new file is equivalent to pytorch_model.bin but safe in the sense that
no arbitrary code can be put into it.
These files also happen to load much faster than their pytorch counterpart:
https://colab.research.google.com/github/huggingface/notebooks/blob/main/safetensors_doc/en/speed.ipynb
The widgets on your model page will run using this model even if this is not merged
making sure the file actually works.
If you find any issues: please report here: https://huggingface.co/spaces/safetensors/convert/discussions
Feel free to ignore this PR.