Trimmed the original dataset to compensate for both the RE baseline and RoBERTa.trex: We split the extra T-REx data collected (for train/val sets of original) into train, dev, test sets.original_rob: We filtered facts in original so that each object is a single token for both BERT and RoBERTa.original: We used the T-REx subset provided by LAMA as our test set and gathered more facts from the original T-REx dataset that we partitioned into train and dev sets.There are a couple different datasets for fact retrieval and relation extraction so here are brief overviews of each: The datasets for sentiment analysis, NLI, fact retrieval, and relation extraction are available to download here Install the required packages pip install -r requirements.txtĪlso download the spacy model python -m spacy download en Evaluation for Fact Retrieval and Relation ExtractionĬonda create -n autoprompt -y python=3.7
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