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Update README.md (#6) 

- Update README.md (ff3875e059f9301a03a6e18d258eb4c1ce5a49a2)


Co-authored-by: Younes Belkada <ybelkada@users.noreply.huggingface.co>
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ArthurZ 2022-06-22 09:53:16 +00:00 committed by system
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@ -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.