Instructions to use jparmet/topic-hf-dropout with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jparmet/topic-hf-dropout with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jparmet/topic-hf-dropout")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jparmet/topic-hf-dropout") model = AutoModelForSequenceClassification.from_pretrained("jparmet/topic-hf-dropout", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from jparmet/topic-hf-dropout: direct link, hf CLI and curl.
- Browser
- Download file 3.64 kB
-
https://huggingface.co/jparmet/topic-hf-dropout/resolve/main/training_args.bin
- Command line
-
hf download hf://jparmet/topic-hf-dropout/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/jparmet/topic-hf-dropout/resolve/main/training_args.bin
3.64 kB
- Xet hash:
- 9098d81520bf3af389550e171576617921e557eb2e5e26e7708f1c0938388cb9
- Size of remote file:
- 3.64 kB
- SHA256:
- 2d323592192c0eb0d2da956adb3854b611d75cd56a7b73b25e7822001c0053ca
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