transformers/model_cards/patrickvonplaten/reformer-crime-and-punish

Reformer Model trained on "Crime and Punishment"

Crime and Punishment text was taken from gs://trax-ml/reformer/crime-and-punishment-2554.txt. Model was trained in flax using colab notebook proposed by authors: https://colab.research.google.com/github/google/trax/blob/master/trax/models/reformer/text_generation.ipynb Weights were converted to Hugging Face PyTorch ReformerModelWithLMHead.

Model is used as a proof of concept that the forward pass works for a ReformerModelWithLMHead. Given that the model was trained only for 30mins on a ~0.5M tokens dataset and has only 320 tokens, the generation results are reasonable:

model = ReformerModelWithLMHead.from_pretrained("patrickvonplaten/reformer-crime-and-punish")
tok = ReformerTokenizer.from_pretrained("patrickvonplaten/reformer-crime-and-punish")
tok.decode(model.generate(tok.encode("A few months later", return_tensors="pt"), do_sample=True,temperature=0.7, max_length=100)[0])

# gives:'A few months later on was more than anything in the flat. 
# “I have already.” “That’s not my notion that he had forgotten him. 
# What does that matter? And why do you mean? It’s only another fellow,” he said as he went out, as though he want'