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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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- sentiment
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# SentiCSE
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This is a
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This model is suitable for English.
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+ Reference Paper: SentiCSE (Main of Coling 2024).
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- sentiment
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# SentiCSE
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This is a RoBERTa-base model trained on MR dataset and finetuned for sentiment analysis with the Sentiment tasks.
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This model is suitable for English.
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+ Reference Paper: SentiCSE (Main of Coling 2024).
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