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
File size: 907 Bytes
1d59e74 bc40060 1d59e74 bc40060 7cbe68d bc40060 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | ---
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。")
``` |