Token Classification
Transformers
Safetensors
English
modernbert
token classification
hallucination detection
Instructions to use KRLabsOrg/lettucedect-base-modernbert-en-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KRLabsOrg/lettucedect-base-modernbert-en-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="KRLabsOrg/lettucedect-base-modernbert-en-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("KRLabsOrg/lettucedect-base-modernbert-en-v1") model = AutoModelForTokenClassification.from_pretrained("KRLabsOrg/lettucedect-base-modernbert-en-v1", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Update pipeline tag, add library name, and improve model card
#1
by nielsr HF Staff - opened
This PR updates the pipeline_tag to question-answering which is more representative of the model's primary use case within RAG-based question-answering systems. It also adds the library_name as transformers. Furthermore, the model card is improved with information from the paper abstract and GitHub repository, providing a more comprehensive overview of the model's purpose, architecture, training data, performance, and usage instructions. Specifically, the paper title, abstract, and bibtex citation are added, and the usage section is expanded with a more detailed example.