Text Classification
Transformers
PyTorch
bert
feature-extraction
custom_code
text-embeddings-inference
Instructions to use Wellcome/WellcomeBertMesh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Wellcome/WellcomeBertMesh with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Wellcome/WellcomeBertMesh", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Wellcome/WellcomeBertMesh", trust_remote_code=True) model = AutoModel.from_pretrained("Wellcome/WellcomeBertMesh", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
Post-2019 MeSH Terms
#5 opened 19 days ago
by
irtazaaslam
Tagging texts which are longer than 512 tokens.
1
#4 opened 11 months ago
by
EmilA
Adding `safetensors` variant of this model
#3 opened over 1 year ago
by
SFconvertbot
Error with sample Code
1
#2 opened over 2 years ago
by
martinWatzinger