Instructions to use alex2awesome/stance-detection-classification-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alex2awesome/stance-detection-classification-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="alex2awesome/stance-detection-classification-model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("alex2awesome/stance-detection-classification-model") model = AutoModelForSequenceClassification.from_pretrained("alex2awesome/stance-detection-classification-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from alex2awesome/stance-detection-classification-model: direct link, hf CLI and curl.
- Browser
- Download file 4.73 kB
-
https://huggingface.co/alex2awesome/stance-detection-classification-model/resolve/main/training_args.bin
- Command line
-
hf download hf://alex2awesome/stance-detection-classification-model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/alex2awesome/stance-detection-classification-model/resolve/main/training_args.bin
4.73 kB
- Xet hash:
- b23b7241f1678d220ad44d6d4721e1142e2f3327bfef56af39d1de70c0706553
- Size of remote file:
- 4.73 kB
- SHA256:
- ba6bf3d96c0b2fd56f62ad1d416e278cba045ea62ebb78f9ec46a8e19ece8939
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.