Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
distilbert
text-embeddings-inference
Instructions to use ebrigham/EYY-Topic-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ebrigham/EYY-Topic-Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ebrigham/EYY-Topic-Classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ebrigham/EYY-Topic-Classification") model = AutoModelForSequenceClassification.from_pretrained("ebrigham/EYY-Topic-Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5dade4db6dad9f7e7ccc7c6cd2cc042c504055879ae795fe52a86fb6bbbecff0
- Size of remote file:
- 268 MB
- SHA256:
- 10850827881f12a59be32bbc3867f9a588cc12fd2ba2faa2c16b6cf9bc79e87e
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