SlowGuess/NAACL_2022_OCR huggingface dataset:
We evaluate the proposed framework on the General Language Understanding Evaluation (GLUE) (Wang et al., 2018) benchmark (https://gluebenchmark.com/). This benchmark consists of the following tasks in English language: Stanford Sentiment Treebank (SST-2)(Socher et al., 2013) and CoLA (Warstadt et al., 2019) for Sentiment Classification. , on Microsoft Research Paraphrase Corpus (MRPC) (Dolan and Brockett, 2005) Quora Question Pairs (QQP) (Chen et al., 2018) for Paraphrase Similarity Matching, Multi-Genre Natural Language Inference (MNLI) (Williams et al., 2017), and Recognizing Textual Entailment (RTE) (Bentivogli et al., 2009) for Natural Language inference.
We also evaluate the performance of the model on SQuAD datasets (https://rajpurkar.github.io/SQuAD-exlore). The datasets are distributed under the CC BY-SA 4.0 license. SQuAD1.1 (Rajpurkar et al., 2016) is a large-scale English reading comprehension that contains 87K question that have answers in the training set(10k in the dev set). SQuAD2.0 (Rajpurkar et al., 2018) combines the questions in SQuAD1.1 with over 50,000 unanswerable questions (130k samples in the training and 11k in the dev set).