Instructions to use eventdata-utd/conflibert-named-entity-recognition with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eventdata-utd/conflibert-named-entity-recognition with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="eventdata-utd/conflibert-named-entity-recognition")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("eventdata-utd/conflibert-named-entity-recognition") model = AutoModelForTokenClassification.from_pretrained("eventdata-utd/conflibert-named-entity-recognition") - Notebooks
- Google Colab
- Kaggle
metadata
license: gpl-3.0
Model Card for Model ID
Conflibert-named-entity-recognition is built upon the foundational Conflibert model. Through rigorous fine-tuning, this enhanced model demonstrates superior capabilities in recognizing and categorizing named entities within textual content. This model is designed to improve the accuracy and efficiency of identifying entities such as persons, organizations, locations, expressions of time and monetary values within text data.
- Finetuned from model : eventdata-utd/ConfliBERT-scr-uncased
- Paper : ConfliBERT: A Pre-trained Language Model for Political Conflict and Violence
- Demo : Colab Notebook