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:
- eaf9d80e66e2155dc04841a8c6d840fed886795f7b9cbf535b191df847273c17
- Size of remote file:
- 536 MB
- SHA256:
- 698032212e12ca747e30a523817d4e5beb7ecf459e8c7654339c4baced1e04ab
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