add support for character-level language models, a new character-level shakespeare dataset, a new config file that shows how to train a character-level baby GPT on it, and adjust the sample function to figure out if it should decode with characters or GPT2 bpe tokens. The current implementation is a bit hacky and basically assumes just these two possibilities. In the future we may want to support more general encoders or decoders.

data/shakespeare_char/prepare.py

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+"""
+Prepare the Shakespeare dataset for character-level language modeling.
+So instead of encoding with GPT-2 BPE tokens, we just map characters to ints.
+Will save train.bin, val.bin containing the ids, and meta.pkl containing the
+encoder and decoder and some other related info.
+"""
+import os
+import pickle
+import requests
+import numpy as np
+
+# download the tiny shakespeare dataset
+if not os.path.exists('input.txt'):
+    data_url = 'https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt'
+    with open('input.txt', 'w') as f:
+        f.write(requests.get(data_url).text)
+
+with open('input.txt', 'r') as f:
+    data = f.read()
+print("length of dataset in characters: ", len(data))
+
+# get all the unique characters that occur in this text
+chars = sorted(list(set(data)))
+vocab_size = len(chars)
+print("all the unique characters:", ''.join(chars))
+print("vocab size:", vocab_size)
+
+# create a mapping from characters to integers
+stoi = { ch:i for i,ch in enumerate(chars) }
+itos = { i:ch for i,ch in enumerate(chars) }
+def encode(s):
+    return [stoi[c] for c in s] # encoder: take a string, output a list of integers
+def decode(l):
+    ''.join([itos[i] for i in l]) # decoder: take a list of integers, output a string
+
+# create the train and test splits
+n = len(data)
+train_data = data[:int(n*0.9)]
+val_data = data[int(n*0.9):]
+
+# encode both to integers
+train_ids = encode(train_data)
+val_ids = encode(val_data)
+print(f"train has {len(train_ids)} tokens")
+print(f"val has {len(val_ids)} tokens")
+
+# export to bin files
+train_ids = np.array(train_ids, dtype=np.uint16)
+val_ids = np.array(val_ids, dtype=np.uint16)
+train_ids.tofile('train.bin')
+val_ids.tofile('val.bin')
+
+# save the meta information as well, to help us encode/decode later
+meta = {
+    'vocab_size': vocab_size,
+    'itos': itos,
+    'stoi': stoi,
+}
+with open('meta.pkl', 'wb') as f:
+    pickle.dump(meta, f)
+
+# length of dataset in characters:  1115394
+# all the unique characters:
+#  !$&',-.3:;?ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz
+# vocab size: 65
+# train has 1003854 tokens
+# val has 111540 tokens

data/shakespeare_char/readme.md

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+
+# tiny shakespeare, character-level
+
+Tiny shakespeare, of the good old char-rnn fame :) Treated on character-level.
+
+After running `prepare.py`:
+
+- train.bin has 1,003,854 tokens
+- val.bin has 111,540 tokens