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
Update README.md
Browse files
README.md
CHANGED
|
@@ -63,7 +63,7 @@ Cosine similarity between "The food is delicious." and "The restaurant lacks a g
|
|
| 63 |
Please cite the reference paper if you use this model.
|
| 64 |
|
| 65 |
```
|
| 66 |
-
@article{
|
| 67 |
title={SentiCSE: A Sentiment-aware Contrastive Sentence Embedding Framework with Sentiment-guided Textual Similarity},
|
| 68 |
author={Kim, Jaemin and Na, Yohan and Kim, Kangmin and Lee, Sangrak and Chae, Dong-Kyu},
|
| 69 |
journal={Proceedings of the 30th International Conference on Computational Linguistics (COLING)},
|
|
|
|
| 63 |
Please cite the reference paper if you use this model.
|
| 64 |
|
| 65 |
```
|
| 66 |
+
@article{2024SentiCSE,
|
| 67 |
title={SentiCSE: A Sentiment-aware Contrastive Sentence Embedding Framework with Sentiment-guided Textual Similarity},
|
| 68 |
author={Kim, Jaemin and Na, Yohan and Kim, Kangmin and Lee, Sangrak and Chae, Dong-Kyu},
|
| 69 |
journal={Proceedings of the 30th International Conference on Computational Linguistics (COLING)},
|