Instructions to use hfl/rbt3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hfl/rbt3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hfl/rbt3")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hfl/rbt3") model = AutoModelForMaskedLM.from_pretrained("hfl/rbt3") - Inference
- Notebooks
- Google Colab
- Kaggle
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tags:
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- bert
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license: "apache-2.0"
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---
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# This is a re-trained 3-layer RoBERTa-wwm-ext model.
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tags:
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- bert
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license: "apache-2.0"
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pipeline_tag: "fill-mask"
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---
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# This is a re-trained 3-layer RoBERTa-wwm-ext model.
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