Instructions to use Parsa/BBB_prediction_classification_IUPAC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Parsa/BBB_prediction_classification_IUPAC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Parsa/BBB_prediction_classification_IUPAC")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Parsa/BBB_prediction_classification_IUPAC") model = AutoModelForSequenceClassification.from_pretrained("Parsa/BBB_prediction_classification_IUPAC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Parsa/BBB_prediction_classification_IUPAC: direct link, hf CLI and curl.
- Browser
- Download file 349 MB
-
https://huggingface.co/Parsa/BBB_prediction_classification_IUPAC/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Parsa/BBB_prediction_classification_IUPAC/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Parsa/BBB_prediction_classification_IUPAC/resolve/main/pytorch_model.bin
349 MB
- Xet hash:
- 8bca11c613801061b697988e221415cb898eb5c25ec8da50ddb7b657459ceaa9
- Size of remote file:
- 349 MB
- SHA256:
- 91fece3b1daceb56a793062fe4c9ef5c3a48ae9783c253535b12fd061bc89014
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.