Instructions to use pere/roberta-base-exp-32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pere/roberta-base-exp-32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="pere/roberta-base-exp-32")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("pere/roberta-base-exp-32") model = AutoModelForMaskedLM.from_pretrained("pere/roberta-base-exp-32", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from pere/roberta-base-exp-32: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/pere/roberta-base-exp-32/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://pere/roberta-base-exp-32/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/pere/roberta-base-exp-32/resolve/main/flax_model.msgpack
1.11 GB
- Xet hash:
- 70d63feb3ba8f563d49a6be1cd4e514c67878d14ab95d2a0c40a6b584fe3fb08
- Size of remote file:
- 1.11 GB
- SHA256:
- c82f12cff68f34e3b029b7c71ea80f035a75b321b53037297dc4a4b3e3f28a2b
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