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")# pip install -U transformers accelerate # 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
Download checkpoint-2000/tokenizer.json from ebrigham/EYY-Topic-Classification: direct link, hf CLI and curl.
- Browser
- Download file 711 kB
-
https://huggingface.co/ebrigham/EYY-Topic-Classification/resolve/main/checkpoint-2000/tokenizer.json
- Command line
-
hf download hf://ebrigham/EYY-Topic-Classification/checkpoint-2000/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/ebrigham/EYY-Topic-Classification/resolve/main/checkpoint-2000/tokenizer.json
711 kB
File too large to display, you can check the raw version instead.