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
- Xet hash:
- 536d381be30aee1052618536607eec3b8120ca337d76ac9f2970185547f68fcd
- Size of remote file:
- 499 MB
- SHA256:
- b53a9c2168bf5e293efe6cfccbe6263844f6bdc2a1d6aed575aa4e2086929e52
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.