Instructions to use fktime/dbert_ai4p with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fktime/dbert_ai4p with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="fktime/dbert_ai4p")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("fktime/dbert_ai4p") model = AutoModelForTokenClassification.from_pretrained("fktime/dbert_ai4p", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from fktime/dbert_ai4p: direct link, hf CLI and curl.
- Browser
- Download file 659 Bytes
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https://huggingface.co/fktime/dbert_ai4p/resolve/main/README.md
- Command line
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hf download hf://fktime/dbert_ai4p/README.md
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curl -L -o README.md https://huggingface.co/fktime/dbert_ai4p/resolve/main/README.md
659 Bytes
| Overall F1 score of 92.67% and precision, recall, and accuracy metrics at 91.86%, 93.49%, and 97.49%, respectively. | |
| -High-Performing Entities: entities like Account Name, Account Number, Age, Company Name, and Email have achieved F1 scores above 95%. | |
| -Entities That Need Improvement: struggled with specific entities like Currency Name (27.3% F1) and IP (0.78% F1). | |
| -Numeric Entities: generally performed well with numeric entities like SSN (99.3% F1), Phone Number (97.5% F1), and Credit Card Issuer (98.1% F1). | |
| -Legal Entities: Job Title and Job Type, which might relate to the legal domain, achieved high F1 scores (around 97%). But need more entities! |