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
| 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](https://arxiv.org/abs/2109.04711): | |
| ``` | |
| @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](https://github.com/bflashcp3f/ProcBERT). |