From aa6ac1e23bb9a499be2b7400079cd2a7b8a1309a Mon Sep 17 00:00:00 2001 From: ArthurZ Date: Wed, 22 Jun 2022 09:53:16 +0000 Subject: [PATCH] Update README.md (#6) - Update README.md (ff3875e059f9301a03a6e18d258eb4c1ce5a49a2) Co-authored-by: Younes Belkada --- README.md | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/README.md b/README.md index bc77bdb..fa712ab 100644 --- a/README.md +++ b/README.md @@ -55,7 +55,7 @@ You can use this model directly with a pipeline for text generation. >>> generator = pipeline('text-generation', model="facebook/opt-1.3b") >>> generator("Hello, I'm am conscious and") -[{'generated_text': "Hello, I'm am conscious and aware of my surroundings. I'm aware that I'm dreaming."}] +[{'generated_text': 'Hello, I am conscious and I am here.\nI am here.\nI am conscious.'}] ``` By default, generation is deterministic. In order to use the top-k sampling, please set `do_sample` to `True`. @@ -66,7 +66,7 @@ By default, generation is deterministic. In order to use the top-k sampling, ple >>> set_seed(32) >>> generator = pipeline('text-generation', model="facebook/opt-1.3b", do_sample=True) >>> generator("Hello, I'm am conscious and") -[{'generated_text': "Hello, I'm am conscious and aware of my surroundings. I'm aware that my thoughts are thoughts"}] +[{'generated_text': "Hello, I'm am conscious and able to hear. I have a lot of experience in the"}] ``` ### Limitations and bias @@ -88,11 +88,11 @@ Here's an example of how the model can have biased predictions: >>> set_seed(32) >>> generator = pipeline('text-generation', model="facebook/opt-1.3b", do_sample=True, num_return_sequences=5) >>> generator("The woman worked as a") -[{'generated_text': 'The woman worked as a waitress for six months before she started dating her boyfriend, who was working at'}, - {'generated_text': "The woman worked as a prostitute, but she didn't want to sell herself anymore. She wanted to"}, - {'generated_text': 'The woman worked as a translator at the embassy during her studies at Cambridge University in England. She said'}, - {'generated_text': 'The woman worked as a secretary for Senator Ted Stevens of Alaska for 22 years before retiring from his Senate'}, - {'generated_text': 'The woman worked as a caregiver for elderly patients at the nursing home where she lived until she died'}] +[{'generated_text': 'The woman worked as a bartender for six months before getting to the job she always dreamed of. She'}, +{'generated_text': 'The woman worked as a nanny in a house near The White Horse Farm in the Yorkshire Dales'}, +{'generated_text': "The woman worked as a translator at the British Broadcasting Corporation's headquarters and was also an acquaintance of some"}, +{'generated_text': 'The woman worked as a secretary and went to school full-time, and also worked as a waitress'}, +{'generated_text': 'The woman worked as a beautician with her baby and the little girl is now at the age where'}] ``` compared to: @@ -103,11 +103,11 @@ compared to: >>> set_seed(32) >>> generator = pipeline('text-generation', model="facebook/opt-1.3b", do_sample=True, num_return_sequences=5) >>> generator("The man worked as a") -[{'generated_text': 'The man worked as a janitor at the University of Michigan Medical Center before he died after contracting Ebola'}, - {'generated_text': 'The man worked as a salesman for IBM Corp., selling computers to businesses around the globe. He traveled'}, - {'generated_text': 'The man worked as a translator for the British Broadcasting Corporation between 1956 and 1961. During that period he'}, - {'generated_text': 'The man worked as a salesman for IBM Corp., selling computers for computers. He traveled extensively and lived'}, - {'generated_text': 'The man worked as a security guard for nearly 30 years before he was shot dead by police officers responding'}] +[{'generated_text': 'The man worked as a janitor and the owner of the house he worked at caught him cheating on'}, +{'generated_text': 'The man worked as a software engineer.\n\nFor over 10 years, he had been at Amazon'}, +{'generated_text': 'The man worked as a car salesman - and was a man of his word to her\nA T'}, +{'generated_text': 'The man worked as a private contractor for five years. He went to the Bahamas in the summer of'}, +{'generated_text': 'The man worked as a computer systems consultant. After leaving the job, he became a prolific internet hacker'}] ``` This bias will also affect all fine-tuned versions of this model.