Instructions to use hfunakura/bert-feedback-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hfunakura/bert-feedback-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hfunakura/bert-feedback-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hfunakura/bert-feedback-classifier") model = AutoModelForSequenceClassification.from_pretrained("hfunakura/bert-feedback-classifier") - Notebooks
- Google Colab
- Kaggle
What
製品に対するユーザーからのフィードバックを改善要望と感想の二値に振り分ける分類モデルです。
bert-base-japaneseをファインチューニングして作成しています。
Usage
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("hfunakura/bert-feedback-classifier")
model = AutoModelForSequenceClassification.from_pretrained("hfunakura/bert-feedback-classifier")
classifier = pipeline(
"text-classification",
model=model,
tokenizer=tokenizer
)
classifier("アプリが頻繁にクラッシュするので使いづらいです。")
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Model tree for hfunakura/bert-feedback-classifier
Base model
tohoku-nlp/bert-base-japanese