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
- Xet hash:
- faf0b8f328e37dda8f62b15ff8de3990a2c147487e884cf5864d5d6f5e40603e
- Size of remote file:
- 433 MB
- SHA256:
- 1783d16de3b3daddcb9a6ddf93ae3656a7308074718856aead130c003fc12023
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