Instructions to use mlcorelib/debertav2-base-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mlcorelib/debertav2-base-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="mlcorelib/debertav2-base-uncased")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("mlcorelib/debertav2-base-uncased") model = AutoModelForMaskedLM.from_pretrained("mlcorelib/debertav2-base-uncased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from mlcorelib/debertav2-base-uncased: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/mlcorelib/debertav2-base-uncased/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://mlcorelib/debertav2-base-uncased/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/mlcorelib/debertav2-base-uncased/resolve/main/flax_model.msgpack
438 MB
- Size of remote file:
- 438 MB
- SHA256:
- ea201fabe466ef7182f1f687fb5be4b62a73d3a78883f11264ff7f682cdb54bf
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