Instructions to use TGA/TwitterTrNewsDetection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TGA/TwitterTrNewsDetection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TGA/TwitterTrNewsDetection")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TGA/TwitterTrNewsDetection") model = AutoModelForSequenceClassification.from_pretrained("TGA/TwitterTrNewsDetection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from TGA/TwitterTrNewsDetection: direct link, hf CLI and curl.
- Browser
- Download file 737 MB
-
https://huggingface.co/TGA/TwitterTrNewsDetection/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://TGA/TwitterTrNewsDetection/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/TGA/TwitterTrNewsDetection/resolve/main/pytorch_model.bin
737 MB
- Xet hash:
- 099c9751492dcc8c8fc815b57cac2c9c7ac84a684001cba0157be16d8f1f3bb2
- Size of remote file:
- 737 MB
- SHA256:
- 4f365e5cc72dcc55a38e41b46c796c629e488e568c7c75a7196e7cc92f800ceb
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