Instructions to use jin-soo/kobert-sentiment-3class-restaurant-fintuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jin-soo/kobert-sentiment-3class-restaurant-fintuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jin-soo/kobert-sentiment-3class-restaurant-fintuned")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jin-soo/kobert-sentiment-3class-restaurant-fintuned") model = AutoModelForSequenceClassification.from_pretrained("jin-soo/kobert-sentiment-3class-restaurant-fintuned") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 524a2b746b381c74d61ef764ae9cb61e186b3af53ba134c5c81a9f9771f581d1
- Size of remote file:
- 371 kB
- SHA256:
- 17dc471055592d3cc9e0a5831e769246a8a001a4d27551c9ed79668173c7b407
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