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:
- ce32f394f008a7f69b87e4d45e0eb97200e90ea12d37e5cc5c4ad111af2060c0
- Size of remote file:
- 268 MB
- SHA256:
- 45b43d136cfce462f5ea5ee146914db119ea04cb7eb5a8109d9f7ace9e78184c
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