Instructions to use ncheng/learning-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ncheng/learning-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ncheng/learning-sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ncheng/learning-sentiment") model = AutoModelForSequenceClassification.from_pretrained("ncheng/learning-sentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e088160d849d9509da233ed0c705b84b01d72905de4fa1d02028dc233e625c05
- Size of remote file:
- 268 MB
- SHA256:
- 8eb9e9f1e4e03aee0ed143865343d7764830757e70ba4c87662709f6c456e77b
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