Instructions to use Shihao-Deng/emotion-classification-distilbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shihao-Deng/emotion-classification-distilbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Shihao-Deng/emotion-classification-distilbert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Shihao-Deng/emotion-classification-distilbert") model = AutoModelForSequenceClassification.from_pretrained("Shihao-Deng/emotion-classification-distilbert") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a6803baa16271ee29b9f6d55185bd1bf8183ab43be5c053f15f8e78fc6afb72f
- Size of remote file:
- 5.84 kB
- SHA256:
- 8bd560f03b5d9d32d11b3a6ab7bcee3085196b6c14bbab582b4bfc7e64df01ee
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