Which CNN Summarization models to use?
Hi@sshleifer,For what task the following models were fine tuned / trained? Can be used for text summarization?sshleifer/student_cnn_12_6sshleifer/student_cnn_6_6Thank you.
2020-07-16T14:13:18Z
These student models are created by copying layers from bart-large-cnn to reduce their size. These are un fine-tuned checkpoints so you’ll need to fine-tune them for summerization. More details can be found herehttps://github.com/huggingface/transformers/tree/master/examples/seq2seq#distilbart
2020-07-16T14:50:16Z
Thank you!
2020-07-16T16:55:16Z
Yes@valhallais 100% correct.More info:Those are the starting point for distillation experimentsthat result in distilbarthttps://docs.google.com/spreadsheets/d/1EkhDMwVO02m8jCD1cG3RoFPLicpcL1GQHTQjfvDYgIM/edit?usp=sharing
2020-07-17T08:55:39Z
Thank you@sshleifer!
2020-07-17T09:46:07Z
Hi Sam,@sshleiferI would like to ask if I can find also such metrics for other modelse.g., airKlizz/, t5-and mrm8488/t5-base-finetuned-summarize-newsThank you.
2020-07-17T10:31:20Z
cc@mrm8488
2020-07-17T10:44:11Z
Add the metrics is in my TODO lists
2020-07-17T15:09:33Z
Yes. It is up to whoever uploaded the model to post their metrics. Please use rouge scores for summarization. Ideally use thenlppackage (nlp.metrics('rouge')or thecalculate_rouge_scorefunction so that we can compare apples to apples, and make sure that beam search params are in your config!Metrics that matter the most:Rouge2 (fscore mid measure)RougeL (fscore mid measure)
2020-11-26T12:33:49Z
Dear all!Which of the following models can be used for summarizating English text out-of-the-box? Without the need to fine-tune any of them.Thank you!Here are the models:google/pegasus-xsumgoogle/pegasus-cnn_dailymailgoogle/pegasus-largegoogle/pegasus-multi_newsgoogle/pegasus-arxivgoogle/pegasus-wikihowgoogle/pegasus-gigawordgoogle/pegasus-pubmedgoogle/pegasus-newsroomgoogle/pegasus-reddit_tifugoogle/pegasus-billsumgoogle/pegasus-aeslcgoogle/roberta2roberta_L-24_bbcgoogle/roberta2roberta_L-24_gigawordgoogle/roberta2roberta_L-24_cnn_daily_mailsshleifer/distill-pegasus-cnn-16-4sshleifer/distill-pegasus-xsum-16-8sshleifer/distill-pegasus-xsum-16-4sshleifer/pegasus-cnn-ft-v2patrickvonplaten/roberta_shared_bbc_xsumpatrickvonplaten/bert2bert_cnn_daily_mailmrm8488/bert-small2bert-small-finetuned-cnn_daily_mail-summarizationyuvraj/xSummyuvraj/summarizer-cnndm
2020-12-11T10:15:59Z
All of these models are fine-tuned for summarization, you could select the model based on your requirements like domain, model size, extractive/abstractive etc.