Instructions to use fbaigt/procbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fbaigt/procbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="fbaigt/procbert")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("fbaigt/procbert") model = AutoModel.from_pretrained("fbaigt/procbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Fan Bai commited on
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README.md
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## ProcBERT
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ProcBERT is a pre-trained language model specifically for procedural text. It was pre-trained on a large-scale procedural corpus (PubMed articles/chemical patents/recipes) containing over 12B tokens and shows great performance on downstream tasks. More details can be found in the following paper:
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@article{Bai2021PretrainOA,
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title={Pre-train or Annotate? Domain Adaptation with a Constrained Budget},
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author={Fan Bai and Alan Ritter and Wei Xu},
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year={2021},
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volume={abs/2109.04711}
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}
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## Usage
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```
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## ProcBERT
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ProcBERT is a pre-trained language model specifically for procedural text. It was pre-trained on a large-scale procedural corpus (PubMed articles/chemical patents/recipes) containing over 12B tokens and shows great performance on downstream tasks. More details can be found in the following paper:
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```
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@article{Bai2021PretrainOA,
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title={Pre-train or Annotate? Domain Adaptation with a Constrained Budget},
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author={Fan Bai and Alan Ritter and Wei Xu},
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year={2021},
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volume={abs/2109.04711}
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}
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```
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## Usage
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```
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