Feature Extraction
Transformers
PyTorch
Safetensors
English
contrastive_bert
contrastive-learning
medical-text
clinical-text
sentence-similarity
triplet-loss
bert
custom_code
Instructions to use nikhil061307/contrastive-learning-bert-26-8-25 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nikhil061307/contrastive-learning-bert-26-8-25 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="nikhil061307/contrastive-learning-bert-26-8-25", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nikhil061307/contrastive-learning-bert-26-8-25", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6a91447f133839777b98c9c6d1995f8488eec2c410ca3724e083cb7709d53f40
- Size of remote file:
- 546 MB
- SHA256:
- af7ae0feb2005f164279d5649efb414ca9e15dcca365f05b689e0e31f2001b87
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