Instructions to use ncbi/MedCPT-Cross-Encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ncbi/MedCPT-Cross-Encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ncbi/MedCPT-Cross-Encoder")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ncbi/MedCPT-Cross-Encoder") model = AutoModelForSequenceClassification.from_pretrained("ncbi/MedCPT-Cross-Encoder", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download added_tokens.json from ncbi/MedCPT-Cross-Encoder: direct link, hf CLI and curl.
- Browser
- Download file 74 Bytes
-
https://huggingface.co/ncbi/MedCPT-Cross-Encoder/resolve/main/added_tokens.json
- Command line
-
hf download hf://ncbi/MedCPT-Cross-Encoder/added_tokens.json
-
curl -L -o added_tokens.json https://huggingface.co/ncbi/MedCPT-Cross-Encoder/resolve/main/added_tokens.json
74 Bytes
| { | |
| "[CLS]": 2, | |
| "[MASK]": 4, | |
| "[PAD]": 0, | |
| "[SEP]": 3, | |
| "[UNK]": 1 | |
| } | |