Instructions to use subbareddyiiit/inria_roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use subbareddyiiit/inria_roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="subbareddyiiit/inria_roberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("subbareddyiiit/inria_roberta") model = AutoModelForMaskedLM.from_pretrained("subbareddyiiit/inria_roberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from subbareddyiiit/inria_roberta: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/subbareddyiiit/inria_roberta/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://subbareddyiiit/inria_roberta/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/subbareddyiiit/inria_roberta/resolve/main/flax_model.msgpack
499 MB
- Xet hash:
- 804c91928615c8cdff32a4f7fc76b2fd1c5a6dfd68af191f17fa3ea978cd7b0d
- Size of remote file:
- 499 MB
- SHA256:
- 2092da2e35561058793478cc7b9be3533c314410b0e52e939b53d25fa18da8e0
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