Text Classification
Transformers
Safetensors
English
French
bert
myocardial-infarction
biomedical
classification
pubmed
scientific-literature
medical-research
text-embeddings-inference
Instructions to use slepape/ArticleTypePrediction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use slepape/ArticleTypePrediction with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="slepape/ArticleTypePrediction")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("slepape/ArticleTypePrediction") model = AutoModelForSequenceClassification.from_pretrained("slepape/ArticleTypePrediction") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 40753b7677bbf8319a66f33c229d08a3ffbf2e6d410cd131b5aefc5df65799b7
- Size of remote file:
- 1.47 kB
- SHA256:
- a9eacf1e72c39a718e9cbac1ea19006f28f483695284b1b4e01710c3f626b127
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.