Instructions to use Gadmz/censor-testing-performance with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Gadmz/censor-testing-performance with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Gadmz/censor-testing-performance")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Gadmz/censor-testing-performance") model = AutoModelForSequenceClassification.from_pretrained("Gadmz/censor-testing-performance", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Gadmz/censor-testing-performance: direct link, hf CLI and curl.
- Browser
- Download file 540 MB
-
https://huggingface.co/Gadmz/censor-testing-performance/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://Gadmz/censor-testing-performance@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Gadmz/censor-testing-performance/resolve/refs%2Fpr%2F1/pytorch_model.bin
540 MB
- Xet hash:
- 00bce8f83a691b490cd1820c7b5efa231642febf5d249b389eff6c2cae2947a5
- Size of remote file:
- 540 MB
- SHA256:
- fb0783bfa7d30e438d14b11204f5a62cdcc0c817b163165fbaef1ce3dd8dd400
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