Instructions to use razent/spbert-mlm-wso-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use razent/spbert-mlm-wso-base with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="razent/spbert-mlm-wso-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("razent/spbert-mlm-wso-base") model = AutoModelForMaskedLM.from_pretrained("razent/spbert-mlm-wso-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from razent/spbert-mlm-wso-base: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/razent/spbert-mlm-wso-base/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://razent/spbert-mlm-wso-base/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/razent/spbert-mlm-wso-base/resolve/main/flax_model.msgpack
433 MB
- Xet hash:
- 697d21f5c3da7d6016e3e47d0661cff973761b781493ba7f8c100133b194696b
- Size of remote file:
- 433 MB
- SHA256:
- 4bf08c2fc7a348a262df279cb0cd35c876638b4ad83eafb85c5be238907ebf2d
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