Instructions to use razent/spbert-mlm-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use razent/spbert-mlm-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="razent/spbert-mlm-base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("razent/spbert-mlm-base") model = AutoModelForMaskedLM.from_pretrained("razent/spbert-mlm-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from razent/spbert-mlm-base: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/razent/spbert-mlm-base/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://razent/spbert-mlm-base/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/razent/spbert-mlm-base/resolve/main/flax_model.msgpack
433 MB
- Xet hash:
- 41cf57b646759a9ea9e9b3409ca68164820d8de2daa8cc5856db42c9c9ddbf9f
- Size of remote file:
- 433 MB
- SHA256:
- 5660f8f0862195439e0727c2477daa86b31d05014a12c12dfbc71df81d05cded
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