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:
- afee65d8bd1892817571b39ce6c1fb47e04d5d00bd5f9f9ba503009d8dd357b0
- Size of remote file:
- 2.48 kB
- SHA256:
- e9a70217d4fa0f8d791e8b5b56916f85ace3c3c218676dfeee4ef36e3ac65785
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