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Recent progress in the Natural Language Processing domain has given usseveral State-of-the-Art (SOTA) pretrained models which can be finetuned forspecific tasks. These large models with billions of parameters trained onnumerous GPUs/TPUs over weeks are leading in the benchmark leaderboards. Inthis paper, we discuss the need for a benchmark for cost and time effectivesmaller models trained on a single GPU. This will enable researchers withresource constraints experiment with novel and innovative ideas ontokenization, pretraining tasks, architecture, fine tuning methods etc. We setup Small-Bench NLP, a benchmark for small efficient neural language modelstrained on a single GPU. Small-Bench NLP benchmark comprises of eight NLP taskson the publicly available GLUE datasets and a leaderboard to track the progressof the community. Our ELECTRA-DeBERTa (15M parameters) small model architectureachieves an average score of 81.53 which is comparable to that of BERT-Base's82.20 (110M parameters). Our models, code and leaderboard are available athttps://github.com/smallbenchnlp

Small-Bench NLP: Benchmark for small single GPU trained models in Natural Language Processing