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import re | |
import sys | |
def insert_spaces(text): | |
text = re.sub(r'([a-zA-Z])([\u4e00-\u9fa5])', r'\1 \2', text) | |
text = re.sub(r'([\u4e00-\u9fa5])([a-zA-Z])', r'\1 \2', text) | |
return text | |
if __name__ == '__main__': |
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# A minimal example of how to implement byte-pair encoding (BPE) tokenizer from scratch in Python. | |
# Reference: https://github.com/karpathy/minbpe | |
# Contact: [email protected] | |
def get_stats(byte_arr): | |
# get the frequency of each byte pair in the text | |
count = {} | |
for pair in zip(byte_arr[:-1], byte_arr[1:]): # e.g. pair: (b'a', b' ') | |
count[pair] = count.get(pair, 0) + 1 |
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# A minimal exmaple of flash attention implemented in Numpy | |
# Contact: bingquanxia AT qq.com | |
import unittest | |
from typing import List | |
import numpy as np | |
import torch |
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# coding=utf-8 | |
# Contact: [email protected] | |
import numpy as np | |
import torch | |
import torch.nn as nn | |
def get_len_mask(b: int, max_len: int, feat_lens: torch.Tensor, device: torch.device) -> torch.Tensor: |
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from argparse import ArgumentParser | |
import editdistance | |
from rich.text import Text | |
from rich.console import Console | |
def edit_dist_dp(gt, hyp): | |
""" | |
A Dynamic Programming based Python program for edit distance problem |