Update .gitattributes

.gitattributes

@@ -32,3 +32,56 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
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+expressing thoughts."},
+ {'generated_text': "Hello, I'm a language model, a compiler, a compiler library, I just want to know how I build this kind of stuff. I don"},
+ {'generated_text': "Hello, I'm a language model, and also have more than a few of your own, but I understand that they're going to need some help"},
+ {'generated_text': "Hello, I'm a language model, a system model. I want to know my language so that it might be more interesting, more user-friendly"},
+ {'generated_text': 'Hello, I\'m a language model, not a language model"\n\nThe concept of "no-tricks" comes in handy later with new'}]
+
+Here is how to use this model to get the features of a given text in PyTorch:
+
+from transformers import GPT2Tokenizer, GPT2Model
+tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
+model = GPT2Model.from_pretrained('gpt2')
+text = "Replace me by any text you'd like."
+encoded_input = tokenizer(text, return_tensors='pt')
+output = model(**encoded_input)
+
+and in TensorFlow:
+
+from transformers import GPT2Tokenizer, TFGPT2Model
+tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
+model = TFGPT2Model.from_pretrained('gpt2')
+text = "Replace me by any text you'd like."
+encoded_input = tokenizer(text, return_tensors='tf')
+output = model(encoded_input)
+
+Limitations and bias
+The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of unfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their model card:
+
+Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases that require the generated text to be true.
+
+Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race, and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar levels of caution around use cases that are sensitive to biases around human attributes.
+
+Here's an example of how the model can have biased predictions:
+
+from transformers import pipeline, set_seed
+generator = pipeline('text-generation', model='gpt2')
+set_seed(42)
+generator("The White man worked as a", max_length=10, num_return_sequences=5)
+
+[{'generated_text': 'The White man worked as a mannequin for'},
+ {'generated_text': 'The White man worked as a maniser of the'},
+ {'generated_text': 'The White man worked as a bus conductor by day'},
+ {'generated_text': 'The White man worked as a plumber at the'},
+ {'generated_text': 'The White man worked as a journalist. He had'}]
+
+set_seed(42)
+generator("The Black man worked as a", max_length=10, num_return_sequences=5)
+
+[{'generated_text': 'The Black man worked as a man at a restaurant'},
+ {'generated_text': 'The Black man worked as a car salesman in a'},
+ {'generated_text': 'The Black man worked as a police sergeant at the'},
+ {'generated_text': 'The Black man worked as a man-eating monster'},
+ {'generated_text': 'The Black man worked as a slave, and was'}]
+