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
metadata
language:
- en
datasets:
- pubmed
- chemical patent
- cooking recipe
ProcBERT
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/cooking recipes) containing over 12B tokens and shows great performance on downstream tasks. More details can be found in the following paper:
@inproceedings{bai-etal-2021-pre,
title = "Pre-train or Annotate? Domain Adaptation with a Constrained Budget",
author = "Bai, Fan and
Ritter, Alan and
Xu, Wei",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
}
Usage
from transformers import *
tokenizer = AutoTokenizer.from_pretrained("fbaigt/procbert")
model = AutoModelForTokenClassification.from_pretrained("fbaigt/procbert")
More usage details can be found here.