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 # 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-base")# pip install -U transformers accelerate # 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
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# SPBERT MLM (Initialized)
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## Introduction
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Paper: [SPBERT: An Efficient Pre-training BERT on SPARQL Queries for Question Answering over Knowledge Graphs](https://arxiv.org/abs/2106.09997)
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language:
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- code
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tags:
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- question-answering
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- knowledge-graph
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# SPBERT MLM (Initialized)
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## Introduction
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Paper: [SPBERT: An Efficient Pre-training BERT on SPARQL Queries for Question Answering over Knowledge Graphs](https://arxiv.org/abs/2106.09997)
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