Models and data needed to run and reproduce results for the acred credibility review system, as described in our paper accepted at the International Semantic Web Conference 2020. Models: semantic_encoder.zip is a RoBERTa base model fine-tuned on STS-B for encoding sentences for semantic similarity saved_fnc1_classifier is a RoBERTa base model with sentence-pair classifier fine-tuned on FNC-1 for stance detection check_worthiness is a RoBERTa base model with sentence classifier fine-tuned on a variety of datasets to predict the checkworthiness of a sentence Data: claims-from-ClaimReviews-45K.csv provides 45K sentences for which we have found ClaimReviews, which serve as ground credibility signals sentences-extractedFrom-Articles-40K.csv provides 40K sentences extracted from a variety of news websites, which serve as ground credibility signals claim_dev_embs_85K_20200426.tar.gz provides embeddings for the 85K sentences in the csv files claimReviews-pruned-45K.jsonl provides a pruned version of the original claimReview. In particular, we do not include the full text of hte claimReview, but only the url, author information and main rating.