enwik8 is a dataset based on Wikipedia and is often used to measure the model's ability to compress data, e.g. in the scope of the Hutter prize: https://en.wikipedia.org/wiki/Hutter_Prize.
reformer-enwik8 was pretrained on the first 90M chars of enwik8 whereas the text was chunked into batches of size 65536 chars (=2^16).
The model's weights were taken from https://console.cloud.google.com/storage/browser/trax-ml/reformer/enwik8 and converted
to Hugging Face's PyTorch ReformerLM model
The model is a language model that operates on characters. Therefore, this model does not need a tokenizer. The following function can instead be used for encoding and decoding:
import torch # Encoding def encode(list_of_strings, pad_token_id=0): max_length = max([len(string) for string in list_of_strings]) # create emtpy tensors attention_masks = torch.zeros((len(list_of_strings), max_length), dtype=torch.long) input_ids = torch.full((len(list_of_strings), max_length), pad_token_id, dtype=torch.long) for idx, string in enumerate(list_of_strings): # make sure string is in byte format if not isinstance(string, bytes): string = str.encode(string) input_ids[idx, :len(string)] = torch.tensor([x + 2 for x in string]) attention_masks[idx, :len(string)] = 1 return input_ids, attention_masks # Decoding def decode(outputs_ids): decoded_outputs =  for output_ids in outputs_ids.tolist(): # transform id back to char IDs < 2 are simply transformed to "" decoded_outputs.append("".join([chr(x - 2) if x > 1 else "" for x in output_ids])) return decoded_outputs
Text can be generated as follows:
from transformers import ReformerModelWithLMHead model = ReformerModelWithLMHead.from_pretrained("google/reformer-enwik8") encoded, attention_masks = encode(["In 1965, Brooks left IBM to found the Department of"]) decode(model.generate(encoded, do_sample=True, max_length=150)) # gives: # In 1965, Brooks left IBM to found the Department of Journalism in 1968. IBM had jurisdiction himself in 1980, while Brooks resolved, nevertheless thro
Note: Language generation using
ReformerModelWithLMHead is not optimized yet and is rather slow.
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