Instructions to use DungHugging/SentimentClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DungHugging/SentimentClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DungHugging/SentimentClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DungHugging/SentimentClassification") model = AutoModelForSequenceClassification.from_pretrained("DungHugging/SentimentClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1fece42d96f74413b6b2ab95e1f083d5e6e342c58ff8f8e4fc16eb6231e6db9e
- Size of remote file:
- 540 MB
- SHA256:
- 6d45a868ad032fb6033e5b44d8f9f3fff0705c567e51c11d03bca2b8f206b5ca
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