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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hfunakura/bert-feedback-classifier") model = AutoModelForSequenceClassification.from_pretrained("hfunakura/bert-feedback-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from hfunakura/bert-feedback-classifier: direct link, hf CLI and curl.
- Browser
- Download file 907 Bytes
-
https://huggingface.co/hfunakura/bert-feedback-classifier/resolve/main/README.md
- Command line
-
hf download hf://hfunakura/bert-feedback-classifier/README.md
-
curl -L -o README.md https://huggingface.co/hfunakura/bert-feedback-classifier/resolve/main/README.md
907 Bytes
metadata
library_name: transformers
tags:
- business
license: apache-2.0
language:
- ja
metrics:
- accuracy
base_model:
- tohoku-nlp/bert-base-japanese
pipeline_tag: text-classification
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。")