Text Classification
Transformers
PyTorch
Safetensors
bert
chemistry
drug-design
synthesis-accessibility
cheminformatics
drug-discovery
selfies
drugs
molecules
compounds
ranger21
madgrad
text-embeddings-inference
Instructions to use gbyuvd/synthaccess-chemselfies with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gbyuvd/synthaccess-chemselfies with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gbyuvd/synthaccess-chemselfies")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("gbyuvd/synthaccess-chemselfies") model = AutoModelForSequenceClassification.from_pretrained("gbyuvd/synthaccess-chemselfies", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from gbyuvd/synthaccess-chemselfies: direct link, hf CLI and curl.
- Browser
- Download file 44.5 MB
-
https://huggingface.co/gbyuvd/synthaccess-chemselfies/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://gbyuvd/synthaccess-chemselfies/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/gbyuvd/synthaccess-chemselfies/resolve/main/pytorch_model.bin
44.5 MB
- Xet hash:
- 1dfb220cdc6e13247847f93bf2c622ec058760e04a32aeea039b9d82a4d3984b
- Size of remote file:
- 44.5 MB
- SHA256:
- fc7d4f4b6d4cdcc3d6c37043ade3ea7922d3608be16603420895bacce6f296d4
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