Instructions to use rifkat/robert_BPE_pubchem10M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rifkat/robert_BPE_pubchem10M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="rifkat/robert_BPE_pubchem10M")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("rifkat/robert_BPE_pubchem10M") model = AutoModelForMaskedLM.from_pretrained("rifkat/robert_BPE_pubchem10M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from rifkat/robert_BPE_pubchem10M: direct link, hf CLI and curl.
- Browser
- Download file 334 MB
-
https://huggingface.co/rifkat/robert_BPE_pubchem10M/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://rifkat/robert_BPE_pubchem10M/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/rifkat/robert_BPE_pubchem10M/resolve/main/pytorch_model.bin
334 MB
- Xet hash:
- a7b724dbf8dee77078fe7f72583d229cde20ca446d2f4827381d5194172a3a91
- Size of remote file:
- 334 MB
- SHA256:
- 663c283486e4c4a283391c2475edf4fb03484dd02792226de527858e6e75a0a6
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