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
|
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
| 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 | |
| ```python | |
| 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。") | |
| ``` |