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 <a href = "https://tdcommons.ai/single_pred_tasks/tox/"> Acute Toxicity LD50 </a> dataset. | |
| For now, for the purpose of prediction, download the model. In the future, an easy colab notebook will be available. |