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:
- 23ff35d7ec6f539e7b46a31c53bd4511a833a152624d6f523ad295fe0ac2d262
- Size of remote file:
- 5.71 kB
- SHA256:
- 362c2471581c039504f3a963a573d5cf7219913796a671892f8544e81b705cfd
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