Instructions to use DeadBeast/roberta-base-pretrained-mr-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DeadBeast/roberta-base-pretrained-mr-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="DeadBeast/roberta-base-pretrained-mr-2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("DeadBeast/roberta-base-pretrained-mr-2") model = AutoModelForMaskedLM.from_pretrained("DeadBeast/roberta-base-pretrained-mr-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from DeadBeast/roberta-base-pretrained-mr-2: direct link, hf CLI and curl.
- Browser
- Download file 250 MB
-
https://huggingface.co/DeadBeast/roberta-base-pretrained-mr-2/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://DeadBeast/roberta-base-pretrained-mr-2/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/DeadBeast/roberta-base-pretrained-mr-2/resolve/main/flax_model.msgpack
250 MB
- Xet hash:
- 0fa1115c6a0104057359f4e0078515cb4d69daf206df19fe1f93d346921f8f56
- Size of remote file:
- 250 MB
- SHA256:
- 22b5b2361774ccfc43c71420ebf4f19ba5353195db09bda4a8dabaf7becfd5bb
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