Create README.md

README.md

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+# SentiCSE
+This is a roBERTa-base model trained on MR dataset and finetuned for sentiment analysis with the Sentiment tasks. 
+This model is suitable for English.
+
+- Reference Paper: SentiCSE (Main of Coling 2024).
+- Git Repo: https://github.com/nayohan/SentiCSE.
+
+```python
+import torch
+from scipy.spatial.distance import cosine
+from transformers import AutoTokenizer, AutoModel
+
+
+tokenizer = AutoTokenizer.from_pretrained("DILAB-HYU/SentiCSE")
+model = AutoModel.from_pretrained("DILAB-HYU/SentiCSE")
+
+# Tokenize input texts
+texts = [
+    "The food is delicious.",
+    "The atmosphere of the restaurant is good.",
+    "The food at the restaurant is devoid of flavor.",
+    "The restaurant lacks a good ambiance."
+]
+inputs = tokenizer(texts, padding=True, truncation=True, return_tensors="pt")
+
+# Get the embeddings
+with torch.no_grad():
+    embeddings = model(**inputs, output_hidden_states=True, return_dict=True).pooler_output
+
+# Calculate cosine similarities
+# Cosine similarities are in [-1, 1]. Higher means more similar
+cosine_sim_0_1 = 1 - cosine(embeddings[0], embeddings[1])
+cosine_sim_0_2 = 1 - cosine(embeddings[0], embeddings[2])
+cosine_sim_0_3 = 1 - cosine(embeddings[0], embeddings[3])
+
+print("Cosine similarity between \"%s\" and \"%s\" is: %.3f" % (texts[0], texts[1], cosine_sim_0_1))
+print("Cosine similarity between \"%s\" and \"%s\" is: %.3f" % (texts[0], texts[2], cosine_sim_0_2))
+print("Cosine similarity between \"%s\" and \"%s\" is: %.3f" % (texts[0], texts[3], cosine_sim_0_3))
+
+```
+Output:
+
+```
+Cosine similarity between "The food is delicious." and "The atmosphere of the restaurant is good." is: 0.942
+Cosine similarity between "The food is delicious." and "The food at the restaurant is devoid of flavor." is: 0.703
+Cosine similarity between "The food is delicious." and "The restaurant lacks a good ambiance." is: 0.656
+```
+
+## BibTeX entry and citation info
+Please cite the reference paper if you use this model.
+
+```
+@article{2024SentiCES,
+  title={SentiCSE: A Sentiment-aware Contrastive Sentence Embedding Framework with Sentiment-guided Textual Similarity},
+  author={Kim, Jaemin and Na, Yohan and Kim, Kangmin and Lee, Sangrak and Chae, Dong-Kyu},
+  journal={Proceedings of the 30th International Conference on Computational Linguistics (COLING)},
+  year={2024},
+}
+```