Text Classification
Transformers
Joblib
Safetensors
deberta-v2
regression
creativity
iclr
text-embeddings-inference
Instructions to use ayarnte/IRM_high_ver with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ayarnte/IRM_high_ver with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ayarnte/IRM_high_ver")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ayarnte/IRM_high_ver") model = AutoModelForSequenceClassification.from_pretrained("ayarnte/IRM_high_ver", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
library_name: transformers
tags:
- regression
- creativity
- iclr
license: mit
pipeline_tag: text-classification
IRM High Ver
DeBERTa-v3-large をベースにした Idea Reward Model(不確実性回帰 + Isotonic 校正)。 入力: タイトル + アブストラクト → 回帰スコア(μ)と 0–1 の報酬にマッピング。