Instructions to use Dilwolf/Kakao_app-kr_sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dilwolf/Kakao_app-kr_sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Dilwolf/Kakao_app-kr_sentiment")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Dilwolf/Kakao_app-kr_sentiment") model = AutoModelForSequenceClassification.from_pretrained("Dilwolf/Kakao_app-kr_sentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Dilwolf/Kakao_app-kr_sentiment: direct link, hf CLI and curl.
- Browser
- Download file 5.05 kB
-
https://huggingface.co/Dilwolf/Kakao_app-kr_sentiment/resolve/main/training_args.bin
- Command line
-
hf download hf://Dilwolf/Kakao_app-kr_sentiment/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Dilwolf/Kakao_app-kr_sentiment/resolve/main/training_args.bin
5.05 kB
- Xet hash:
- 8f89b13df9ef9812a7ed6f913703ecceb9439dbab44014de5baa361f5086426d
- Size of remote file:
- 5.05 kB
- SHA256:
- f5348340fc050105137e9c74c98f5f29bfd3e364aa8e4b7025ea11d2c052bc63
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.