Feature Extraction
Transformers
PyTorch
TensorFlow
JAX
Safetensors
English
bert
biomedical
lexical semantics
bionlp
biology
science
embedding
entity linking
Instructions to use cambridgeltl/SapBERT-from-PubMedBERT-fulltext with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cambridgeltl/SapBERT-from-PubMedBERT-fulltext with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="cambridgeltl/SapBERT-from-PubMedBERT-fulltext")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("cambridgeltl/SapBERT-from-PubMedBERT-fulltext") model = AutoModel.from_pretrained("cambridgeltl/SapBERT-from-PubMedBERT-fulltext", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Expected input for the hosted inference API
#1
by jdsouza - opened
Dear authors,
what is the input expected for the hosted inference API of your model? And how do we interpret the resulting output dimensions?
Many thanks for your support in advance.
Hi,
We have added the following information:
The input should be a string of biomedical entity names, e.g., "covid infection" or "Hydroxychloroquine". The [CLS] embedding of the last layer is regarded as the output.
Thanks!
fl399 changed discussion status to closed