Text Classification
Transformers
PyTorch
English
roberta
feature-extraction
sentiment
text-embeddings-inference
Instructions to use DILAB-HYU/SentiCSE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DILAB-HYU/SentiCSE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DILAB-HYU/SentiCSE")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("DILAB-HYU/SentiCSE") model = AutoModel.from_pretrained("DILAB-HYU/SentiCSE") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6885a0455384f97c55352d3646022b9ad92ad1454c3e2f51865fc673288643fc
- Size of remote file:
- 499 MB
- SHA256:
- 9656747eba27402e504e1c5328a0f7ce5da965a3a06b8e357c406bd72456cae0
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