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