Instructions to use voidful/albert_chinese_xlarge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use voidful/albert_chinese_xlarge with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="voidful/albert_chinese_xlarge")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("voidful/albert_chinese_xlarge") model = AutoModelForMaskedLM.from_pretrained("voidful/albert_chinese_xlarge", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Adding `safetensors` variant of this model (#1)
- Adding `safetensors` variant of this model (419992462883beca6e170e83ff77c5fd95bb41c7) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>4d76e6d
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update auto tokenizer support
86cad98
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Migrate model card from transformers-repo
Read announcement at https://discuss.huggingface.co/t/announcement-all-model-cards-will-be-migrated-to-hf-co-model-repos/2755 Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/voidful/albert_chinese_xlarge/README.mda28d99d
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Update config.json
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Update config.json
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Update vocab.txt
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Update pytorch_model.bin
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initial commit
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