Feature Extraction
Transformers
PyTorch
Safetensors
English
modernbert
clinical-notes
contrastive-learning
sentence-embeddings
medical-nlp
clinical-modernbert
Eval Results (legacy)
text-embeddings-inference
Instructions to use nikhil061307/contrastive-learning-bert-added-token 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 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")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nikhil061307/contrastive-learning-bert-added-token") model = AutoModel.from_pretrained("nikhil061307/contrastive-learning-bert-added-token") - Notebooks
- Google Colab
- Kaggle
Ctrl+K