![]() ![]() Note: each of the configurable settings are marked with a comment. To change evaluation settings, go to scripts/run_experiments.py and update the configurable values accordingly. Update the data/relations.jsonl file with your own automatically generated prompts 3. Mkdir pre-trained_language_models/roberta For BERT, stick and to each end of the template. BERT or RoBERTa) you choose to generate prompts, the special tokens will be different. Each trigger token in the set of trigger tokens that are shared across all prompts is denoted by. denotes the placement of a special token that will be used to "fill-in-the-blank" by the language model. The example above is a template for generating fact retrieval prompts with 3 trigger tokens where is a placeholder for the subject in any (subject, relation, object) triplet in fact retrieval. Generating Prompts Quick Overview of TemplatesĪ prompt is constructed by mapping things like the original input and trigger tokens to a template that looks something like We also excluded relations P527 and P1376 because the RE baseline doesn’t consider them. 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 ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |