ConfliBERT Tasks and Applications
Collection
Explore how ConfliBERT is applied across different NLP tasks such as Named Entity Recognition, Classification, and Multi-label Classification. • 4 items • Updated
# 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")Quick Links
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
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="eventdata-utd/conflibert-named-entity-recognition")