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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 73e5bf8fed8613f020190d767e5d27bd4f4bcba4a90181fb74b2c5485478e79d
- Size of remote file:
- 438 MB
- SHA256:
- 8d5c5c3708cdfa9e9a61dfaa2e11b5a29c598444694702ceba0bab2ee4bd39b4
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