Instructions to use subbareddyiiit/TeRobeRta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use subbareddyiiit/TeRobeRta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="subbareddyiiit/TeRobeRta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("subbareddyiiit/TeRobeRta") model = AutoModelForMaskedLM.from_pretrained("subbareddyiiit/TeRobeRta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from subbareddyiiit/TeRobeRta: direct link, hf CLI and curl.
- Browser
- Download file 959 MB
-
https://huggingface.co/subbareddyiiit/TeRobeRta/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://subbareddyiiit/TeRobeRta/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/subbareddyiiit/TeRobeRta/resolve/main/flax_model.msgpack
959 MB
- Xet hash:
- 2b004e0ef160b4259295d48a9a69cf8efe9d7c55453008b5a50328bbafaddb5f
- Size of remote file:
- 959 MB
- SHA256:
- e23834d7a75eae0fb3de77d356d56f7d1de7390046b954684832b91d75aa3262
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