Instructions to use knowledgator/SMILES-DeBERTa-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use knowledgator/SMILES-DeBERTa-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="knowledgator/SMILES-DeBERTa-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("knowledgator/SMILES-DeBERTa-base") model = AutoModelForMaskedLM.from_pretrained("knowledgator/SMILES-DeBERTa-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from knowledgator/SMILES-DeBERTa-base: direct link, hf CLI and curl.
- Browser
- Download file 4.03 kB
-
https://huggingface.co/knowledgator/SMILES-DeBERTa-base/resolve/main/training_args.bin
- Command line
-
hf download hf://knowledgator/SMILES-DeBERTa-base/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/knowledgator/SMILES-DeBERTa-base/resolve/main/training_args.bin
4.03 kB
- Xet hash:
- f68d355ace70818419d0b55cd183fa1ec453495005637230b586aa8b628126d4
- Size of remote file:
- 4.03 kB
- SHA256:
- f2d3c1c4127091e9dc4499212d04192a306010ac65275900bc06c6fd797e96b8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.