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:
- c595bfcc3880b5f59dc8095ebaa29edd3bb7cba5df9d00b592ac22c3f23e35f5
- Size of remote file:
- 876 MB
- SHA256:
- c6b9b94786801649429d988aa2cd717776e7f215638b2537db536033065d5c42
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