Instructions to use rifkat/robert_BPE_zinc100k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rifkat/robert_BPE_zinc100k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="rifkat/robert_BPE_zinc100k")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("rifkat/robert_BPE_zinc100k") model = AutoModelForMaskedLM.from_pretrained("rifkat/robert_BPE_zinc100k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from rifkat/robert_BPE_zinc100k: direct link, hf CLI and curl.
- Browser
- Download file 2.48 kB
-
https://huggingface.co/rifkat/robert_BPE_zinc100k/resolve/main/training_args.bin
- Command line
-
hf download hf://rifkat/robert_BPE_zinc100k/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/rifkat/robert_BPE_zinc100k/resolve/main/training_args.bin
2.48 kB
- Xet hash:
- 3512751abce005e2405f615973c707e80ba2506ef4d17d95f835b4632ed054c0
- Size of remote file:
- 2.48 kB
- SHA256:
- 6409118538e2863a13ce2f00fb5bd5556d8770e17cdf1fb3aa09db5203fc03d3
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