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
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Download README.md from Parsa/LD50-prediction: direct link, hf CLI and curl.
- Browser
- Download file 263 Bytes
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https://huggingface.co/Parsa/LD50-prediction/resolve/main/README.md
- Command line
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hf download hf://Parsa/LD50-prediction/README.md
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curl -L -o README.md https://huggingface.co/Parsa/LD50-prediction/resolve/main/README.md
263 Bytes
Toxicity LD50 prediction (regression model) based on Acute Toxicity LD50 dataset. For now, for the purpose of prediction, download the model. In the future, an easy colab notebook will be available.