Token Classification
Transformers
Safetensors
English
bert
financial NLP
named entity recognition
sequence labeling
structured extraction
hierarchical taxonomy
XBRL
iXBRL
SEC filings
financial-information-extraction
Instructions to use AAU-NLP/Cal-BERT-SL1000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AAU-NLP/Cal-BERT-SL1000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="AAU-NLP/Cal-BERT-SL1000")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("AAU-NLP/Cal-BERT-SL1000") model = AutoModelForTokenClassification.from_pretrained("AAU-NLP/Cal-BERT-SL1000") - Notebooks
- Google Colab
- Kaggle
Add paper link and citation, update official repository
#1
by nielsr HF Staff - opened
Hi! I'm Niels from the community science team at Hugging Face.
I'm opening this PR to improve the model card for Cal-BERT-SL1000. These changes help users discover the research context and properly cite your work.
Specifically, I have:
- Linked the model card to the official paper: HiFi-KPI: A Dataset for Hierarchical KPI Extraction from Earnings Filings.
- Updated the GitHub link to point to the official organizational repository.
- Standardized the YAML metadata.
- Added a BibTeX citation section.
This ensures that the model is correctly attributed to the authors and the associated research paper.
rasmus-aau changed pull request status to merged