Instructions to use AiLab-IMCS-UL/lvbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AiLab-IMCS-UL/lvbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="AiLab-IMCS-UL/lvbert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("AiLab-IMCS-UL/lvbert") model = AutoModel.from_pretrained("AiLab-IMCS-UL/lvbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Updated README
Browse files
README.md
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## Pretraining
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We used the BERT-base configuration with 12 layers, 768 hidden units, 12 heads,
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## Citation
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## Pretraining
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We used the BERT-base configuration with 12 layers, 768 hidden units, 12 heads, 512 sequence length, 128 mini-batch size and 32k token vocabulary.
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## Citation
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