Update README.md

README.md

@@ -23,9 +23,23 @@ set a seed for reproducibility:
 
 ```python
 >>> from transformers import pipeline, set_seed
+>>> # It is important to include the bad_words_ids=[[0,2]] if you want this model to stay on topic.
+>>> # Otherwise, the model may generate start and end tokens followed by text that is not relevant to
+>>> # the previous text.
 >>> generator = pipeline('text-generation', model='olm/olm-gpt2-oct-2022')
 >>> set_seed(42)
+>>> # This example also illustrates that sometimes our model generates
+>>> # bloggy/spammy/webb-y things, even though it gets higher evaluation results
+>>> # than the original GPT-2 accross a variety of benchmarks. See the first output.
 >>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5)
+Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.
+[
+{'generated_text': "Hello, I'm a language model, but you can take me if I want.\nReplyDelete\nReplies\nReply\nAnonymous October 17, 2011"},
+{'generated_text': "Hello, I'm a language model, and here's some useful news for you all: The release date for the new release of"},
+{'generated_text': "Hello, I'm a language model, I'm not a developer or anybody who's working on those. I'm a freelancer... I"},
+{'generated_text': "Hello, I'm a language model, a language analyst, and a language system designer. I'm just curious about the"},
+{'generated_text': "Hello, I'm a language model, I'm passionate about languages, but I don't understand how my system works, the interaction"}
+]
 ```
 
 Here is how to use this model to get the features of a given text in PyTorch:
@@ -33,7 +47,7 @@ Here is how to use this model to get the features of a given text in PyTorch:
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
 tokenizer = AutoTokenizer.from_pretrained('olm/olm-gpt2-oct-2022')
-model = AutoModelForCausalLM.from_pretrained('gpt2')
+model = AutoModelForCausalLM.from_pretrained('olm/olm-gpt2-oct-2022')
 text = "Replace me by any text you'd like."
 encoded_input = tokenizer(text, return_tensors='pt')
 output = model(**encoded_input)