Instructions to use foundkim/topic_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use foundkim/topic_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="foundkim/topic_classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("foundkim/topic_classifier") model = AutoModelForSequenceClassification.from_pretrained("foundkim/topic_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from foundkim/topic_classifier: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/foundkim/topic_classifier/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://foundkim/topic_classifier/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/foundkim/topic_classifier/resolve/main/pytorch_model.bin
433 MB
- Xet hash:
- b984b2cd782c7b803ae481d10906343b772589c9d8895abfca1c66857625616b
- Size of remote file:
- 433 MB
- SHA256:
- 9a7107540fe6440dda4c2e3896dc9584de987a1e92d835a4520a71ce6c3c7611
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.