Instructions to use rifkat/pubchem_1M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rifkat/pubchem_1M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="rifkat/pubchem_1M")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("rifkat/pubchem_1M") model = AutoModelForMaskedLM.from_pretrained("rifkat/pubchem_1M") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f462fee6246257d4e3a9c0cbc09f6f7c04950c9255877b3e111eff17da9e06fb
- Size of remote file:
- 334 MB
- SHA256:
- 3d786c79b6c644a07af010aa530fdcef684df92cff0af755819c9ef0d6941d11
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