Text Classification
Transformers
Safetensors
bert
multilingual
multi-label-classification
community-notes
topic-classification
text-embeddings-inference
Instructions to use ychuai/community-notes-topic-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ychuai/community-notes-topic-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ychuai/community-notes-topic-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ychuai/community-notes-topic-classifier") model = AutoModelForSequenceClassification.from_pretrained("ychuai/community-notes-topic-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 516 Bytes
cec807d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | {
"pipeline_version": "post-with-all-notes-v1",
"base_model": "Twitter/twhin-bert-base",
"categories": [
"Politics and Elections",
"War and Geopolitics",
"Health and Medicine",
"Economy and Finance",
"Technology and AI",
"Crime and Public Safety",
"Sports and Games",
"Celebrity and Entertainment",
"Religion and Spirituality",
"Gender and Identity"
],
"threshold": 0.5,
"max_length": 512,
"stride": 64,
"pooling": "max_logits",
"gpt_model": "gpt-5.4-mini"
} |