SLIM: Sentiment Lexicon Integrated Meta-training for Low-resource Sentiment Analysis

In this repository, we provide checkpoints of the meta-trained models developed using our SLIM framework. SLIM models are optimized for sentiment classification and rating classification tasks in few-shot settings. You can download the models via Hugging Face or Google Drive.

Hugging Face

Use the following code to load one of the models listed below.

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("hjkim811/SLIM-W_polar")
model = AutoModelForSequenceClassification.from_pretrained("hjkim811/SLIM-W_polar")

• SLIM-C_boolean: https://huggingface.co/hjkim811/SLIM-C_boolean
• SLIM-C_mixed: https://huggingface.co/hjkim811/SLIM-C_mixed
• SLIM-C_polar: https://huggingface.co/hjkim811/SLIM-C_polar
• SLIM-W_boolean: https://huggingface.co/hjkim811/SLIM-W_boolean
• SLIM-W_mixed: https://huggingface.co/hjkim811/SLIM-W_mixed
• SLIM-W_polar: https://huggingface.co/hjkim811/SLIM-W_polar

Google Drive

• All models: https://bit.ly/slim_models

Notes

• SLIM-W_polar is the best-performing model and is recommended for use.
• More details about the framework and the models will be available in our paper 'Sentiment Lexicon Integrated Meta-training for Low-resource Sentiment Analysis' (under review).