Create README.md

README.md

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+## About
+This is a curated subset of 3 representative samples per class (77 classes in total) for the Banking77 dataset, as collected by a domain expert.
+It was used in the paper "Making LLMs Worth Every Penny: Resource-Limited Text Classification in Banking", published in ACM ICAIF 2023 (https://arxiv.org/abs/2311.06102).
+Our findings show that Few-Shot Text Classification on representative samples are better than randomly selected samples.
+
+## Citation
+
+```
+@inproceedings{10.1145/3604237.3626891,
+author = {Loukas, Lefteris and Stogiannidis, Ilias and Diamantopoulos, Odysseas and Malakasiotis, Prodromos and Vassos, Stavros},
+title = {Making LLMs Worth Every Penny: Resource-Limited Text Classification in Banking},
+year = {2023},
+isbn = {9798400702402},
+publisher = {Association for Computing Machinery},
+address = {New York, NY, USA},
+url = {https://doi.org/10.1145/3604237.3626891},
+doi = {10.1145/3604237.3626891},
+pages = {392–400},
+numpages = {9},
+keywords = {Anthropic, Cohere, OpenAI, LLMs, NLP, Claude, GPT, Few-shot},
+location = {Brooklyn, NY, USA},
+series = {ICAIF '23}
+}
+```
+
+---
+language:
+- en
+Tags:
+- banking77
+- classification
+- conversational
+---