Instructions to use haisongzhang/roberta-tiny-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use haisongzhang/roberta-tiny-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="haisongzhang/roberta-tiny-cased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("haisongzhang/roberta-tiny-cased") model = AutoModel.from_pretrained("haisongzhang/roberta-tiny-cased", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from haisongzhang/roberta-tiny-cased: direct link, hf CLI and curl.
- Browser
- Download file 112 MB
-
https://huggingface.co/haisongzhang/roberta-tiny-cased/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://haisongzhang/roberta-tiny-cased/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/haisongzhang/roberta-tiny-cased/resolve/main/flax_model.msgpack
112 MB
- Xet hash:
- cd30768a2a98b64b9b4e59f75f759a152312cd3b292dcc0f4d1569a9b97f0feb
- Size of remote file:
- 112 MB
- SHA256:
- 8b7e800d927d8324c85525987dd9276390c70f59eeeee5177d3290493475f450
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