Text Classification
Transformers
Safetensors
bert
citation-prediction
m3
repository-library
research-library
t1_metadata
text-embeddings-inference
Instructions to use PeytonT/citation-prediction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PeytonT/citation-prediction with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="PeytonT/citation-prediction")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("PeytonT/citation-prediction") model = AutoModelForSequenceClassification.from_pretrained("PeytonT/citation-prediction", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- def47ec433a8d7035e9e8306d573632d8c111af7ff1a29ee025ca163c3d8f4de
- Size of remote file:
- 5.78 kB
- SHA256:
- 6ff851117f464788fdca4daebc524263b2ebb2deb1d00c23f593582a6c1441af
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