Thanks for sharing! Looks great, care to share your thoughts on the model decisions in the models.py file. I assume you have a take on the speed vs storage cost

Thanks for sharing! Looks great, care to share your thoughts on the model decisions in the models.py file. I assume you have a take on the speed vs storage costs vs relevance quality (and/or licensing) of these models vs others here: https://huggingface.co/spaces/mteb/leaderboard https://huggingface.co/spaces/mteb/leaderboard and here: https://www.sbert.net/docs/pretrained_models.html https://www.sbert.net/docs/pretrained_models.html: bi_encoder = SentenceTransformer('multi-qa-MiniLM-L6-cos-v1') cross_encoder_small = CrossEncoder('cross-encoder/ms-marco-TinyBERT-L-2-v2') cross_encoder_large = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2') qa_model = pipeline('question-answering', model='deepset/roberta-base-squad2')