Instructions to use Parsa/LD50-prediction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Parsa/LD50-prediction with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Parsa/LD50-prediction")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Parsa/LD50-prediction") model = AutoModelForSequenceClassification.from_pretrained("Parsa/LD50-prediction", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Parsa/LD50-prediction: direct link, hf CLI and curl.
- Browser
- Download file 13.7 MB
-
https://huggingface.co/Parsa/LD50-prediction/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Parsa/LD50-prediction/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Parsa/LD50-prediction/resolve/main/pytorch_model.bin
13.7 MB
- Xet hash:
- c08963207ac614d5183dfc92e385da3e333bd46242eff311d43aed619a2f9637
- Size of remote file:
- 13.7 MB
- SHA256:
- 44645d224055967bc2b56f73abca7c653a944eea8736ddf79b00197e10099fe0
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