Instructions to use dhtocks/Topic-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dhtocks/Topic-Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dhtocks/Topic-Classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dhtocks/Topic-Classification") model = AutoModelForSequenceClassification.from_pretrained("dhtocks/Topic-Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from dhtocks/Topic-Classification: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/dhtocks/Topic-Classification/resolve/refs%2Fpr%2F1/model.safetensors
- Command line
-
hf download hf://dhtocks/Topic-Classification@refs/pr/1/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/dhtocks/Topic-Classification/resolve/refs%2Fpr%2F1/model.safetensors
499 MB
- Xet hash:
- 8ffeb377a02c00c685a92811f03bdc9c0d793aa9802a19215dda850745e88e86
- Size of remote file:
- 499 MB
- SHA256:
- dd04baf7fda3cea8da974ea7b031da7b5b301ef64e07ef7d8ffc71a651430a0e
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