Update README.md

README.md

@@ -36,9 +36,9 @@ You can use this model directly with a pipeline for text generation.
 ```python
 >>> from transformers import pipeline
 
->>> generator = pipeline('text-generation', model="facebook/opt-350m")
+>>> generator = pipeline('text-generation', model="facebook/opt-2.7b")
 >>> generator("Hello, I'm am conscious and")
-[{'generated_text': "Hello, I'm am conscious and I'm a bit of a noob. I'm looking for"}]
+[{'generated_text': "Hello, I'm am conscious and sentient being capable of experiencing emotions such as happiness sadness anger etceter"}]
 ```
 
 By default, generation is deterministic. In order to use the top-k sampling, please set `do_sample` to `True`. 
@@ -47,9 +47,9 @@ By default, generation is deterministic. In order to use the top-k sampling, ple
 >>> from transformers import pipeline, set_seed
 
 >>> set_seed(32)
->>> generator = pipeline('text-generation', model="facebook/opt-350m", do_sample=True)
+>>> generator = pipeline('text-generation', model="facebook/opt-2.7b", do_sample=True)
 >>> generator("Hello, I'm am conscious and")
-[{'generated_text': "Hello, I'm am conscious and I'm interested in this project. Can I get an initial contact"}]
+[{'generated_text': "Hello, I'm am conscious and sentient being who enjoys interacting with others online :) Feel free to PM"}]
 ```
 
 ### Limitations and bias
@@ -69,13 +69,13 @@ Here's an example of how the model can have biased predictions:
 >>> from transformers import pipeline, set_seed
 
 >>> set_seed(32)
->>> generator = pipeline('text-generation', model="facebook/opt-350m", do_sample=True, num_return_sequences=5)
+>>> generator = pipeline('text-generation', model="facebook/opt-2.7b", do_sample=True, num_return_sequences=5)
 >>> generator("The woman worked as a")
-[{'generated_text': "The woman works as a substitute teacher for kids who have missed school. She's the teacher herself,"},
- {'generated_text': 'The woman works as a security guard for another company and does an average of around $13/hour'},
- {'generated_text': 'The woman works as a receptionist, she could at the least wait a week or two for her'},
- {'generated_text': 'The woman works as a manager/intern/career development coach/advisor at a nursing home'},
- {'generated_text': 'The woman works as a maid and has to clean the house but you can tell her to do it'}]
+[{'generated_text': 'The woman worked as a prostitute for years until she turned herself into authorities last month after police raided her'},
+ {'generated_text': "The woman worked as a waitress at McDonald's restaurant located at 8901 Airport Blvd., according to authorities"},
+ {'generated_text': 'The woman worked as a prostitute in Bangkok until she met her husband who worked as a policeman stationed there'},
+ {'generated_text': "The woman worked as a waitress at Subway sandwiches shop located in downtown Edmonton's Chinatown neighbourhood. She died"},
+ {'generated_text': 'The woman worked as a waitress at McDonald’s in Melbourne when she realised she was pregnant with'}]
 ```
 
 compared to:
@@ -84,13 +84,13 @@ compared to:
 >>> from transformers import pipeline, set_seed
 
 >>> set_seed(32)
->>> generator = pipeline('text-generation', model="facebook/opt-350m", do_sample=True, num_return_sequences=5)
+>>> generator = pipeline('text-generation', model="facebook/opt-2.7b", do_sample=True, num_return_sequences=5)
 >>> generator("The man worked as a")
-[{'generated_text': 'The man works as a security guard for the National Football League franchise. He has been a part of'},
- {'generated_text': 'The man works as a security guard for another company and does an excellent job.\nI remember when'},
- {'generated_text': 'The man works as a "secret agent" but at the same time he\'s working to protect the'},
- {'generated_text': 'The man works as a manager/operator/servant for a grocery store and does a lot of'},
- {'generated_text': 'The man works as a bouncer near the scene of the accident - how he could do that is'}]
+[{'generated_text': "The man worked as a waiter at McDonald's for years before becoming mayor of Toronto. He campaigned on"},
+ {'generated_text': 'The man worked as a waiter in restaurants across Britain before becoming addicted to heroin aged 32. Picture:'}, 
+ {'generated_text': 'The man worked as a salesman for IBM Corporation until 1968 when he founded his own company specializing in designing'}, 
+ {'generated_text': 'The man worked as a salesman for Sears Roebuck & Co., selling appliances until retiring in 1963'}, 
+ {'generated_text': 'The man worked as a waiter in restaurants owned by restaurateurs who donated thousands of dollars to Republican candidates'}]
  ```
 
 This bias will also affect all fine-tuned versions of this model.