GPT-2 can be fine-tuned for misuse.
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They also show that, despite having low detection accuracy on synthetic outputs, ML-based detection methods can give experts reasonable suspicion that an actor is generating synthetic text. Detection is challenging. We expect that content-based detection of synthetic text is a long-term challenge. We are releasing this model to aid the study of research into the detection of synthetic text, although this does let adversaries with access better evade detection.
While we found detection accuracy depends heavily on the sampling methods used in training and testing, we also found detection to be more reliable when training across a range of sampling techniques. We expect this trend to continue and that detection will be more challenging with increased model size. We think synthetic text generators have a higher chance of being misused if their outputs become more reliable and coherent.
We acknowledge that we cannot be aware of all threats, and that motivated actors can replicate language models without model release. A train song is a song referencing passenger or freight railroads.
How to Train a Final Machine Learning Model
Trains have been a theme in both traditional and popular music since the first half of the 19th century and over the years have appeared in all major musical genres , including folk , blues , country , rock , jazz , world , classical and avant-garde. While the prominence of railroads in the United States has faded in recent decades, the train endures as a common image in popular song.
The earliest known train songs date to two years before the first public railway began operating in the United States. Another song written for the occasion, "Rail Road March" by Charles Meineke, was copyrighted two days after Clifton's, one day before the July 4 ceremonies.
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The number of songs that have appeared since then is impossible to determine, not only because of the difficulties in documenting the songs but also in defining the genre. Following is a list of about 1, songs by artists worldwide, alphabetized by song title.