Text Classification
Transformers
Safetensors
Chinese
qwen3
text-generation
cross-encoder
reranker
event-coreference
event-deduplication
chinese
news-clustering
knowledge-distillation
calibrated-probabilities
text-embeddings-inference
Instructions to use MaYiding/EventTwin with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MaYiding/EventTwin with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MaYiding/EventTwin")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("MaYiding/EventTwin") model = AutoModelForCausalLM.from_pretrained("MaYiding/EventTwin", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
EventTwin v1.0: calibrated Chinese same-event cross-encoder (568M, T=0.824) d7204f3
MaYiding commited on