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/Pre-BERT-SL1000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AAU-NLP/Pre-BERT-SL1000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="AAU-NLP/Pre-BERT-SL1000")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("AAU-NLP/Pre-BERT-SL1000") model = AutoModelForTokenClassification.from_pretrained("AAU-NLP/Pre-BERT-SL1000") - Notebooks
- Google Colab
- Kaggle
Add paper link and improve model card metadata
#1
by nielsr HF Staff - opened
Hi! I'm Niels from the community science team at Hugging Face.
I'm opening this PR to link your model to its research paper: HiFi-KPI: A Dataset for Hierarchical KPI Extraction from Earnings Filings. Linking the model to its paper helps users understand the context, methodology, and dataset used.
I have also updated the metadata to follow standard conventions and updated the GitHub repository link to point to the official organization repository.
rasmus-aau changed pull request status to merged