mirror of
https://github.com/explosion/spaCy.git
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130 lines
3.9 KiB
Python
130 lines
3.9 KiB
Python
from os import path
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import io
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import json
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import re
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from .attrs import TAG, HEAD, DEP, ENT_IOB, ENT_TYPE
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DATA_DIR = path.join(path.dirname(__file__), '..', 'data')
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def normalize_slice(length, start, stop, step=None):
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if not (step is None or step == 1):
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raise ValueError("Stepped slices not supported in Span objects."
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"Try: list(tokens)[start:stop:step] instead.")
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if start is None:
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start = 0
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elif start < 0:
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start += length
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start = min(length, max(0, start))
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if stop is None:
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stop = length
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elif stop < 0:
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stop += length
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stop = min(length, max(start, stop))
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assert 0 <= start <= stop <= length
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return start, stop
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def utf8open(loc, mode='r'):
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return io.open(loc, mode, encoding='utf8')
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def read_lang_data(data_dir):
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with open(path.join(data_dir, 'specials.json')) as file_:
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tokenization = json.load(file_)
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prefix = read_prefix(data_dir)
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suffix = read_suffix(data_dir)
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infix = read_infix(data_dir)
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return tokenization, prefix, suffix, infix
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def read_prefix(data_dir):
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with utf8open(path.join(data_dir, 'prefix.txt')) as file_:
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entries = file_.read().split('\n')
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expression = '|'.join(['^' + re.escape(piece) for piece in entries if piece.strip()])
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return expression
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def read_suffix(data_dir):
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with utf8open(path.join(data_dir, 'suffix.txt')) as file_:
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entries = file_.read().split('\n')
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expression = '|'.join([piece + '$' for piece in entries if piece.strip()])
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return expression
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def read_infix(data_dir):
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with utf8open(path.join(data_dir, 'infix.txt')) as file_:
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entries = file_.read().split('\n')
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expression = '|'.join([piece for piece in entries if piece.strip()])
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return expression
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def read_tokenization(lang):
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loc = path.join(DATA_DIR, lang, 'tokenization')
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entries = []
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seen = set()
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with utf8open(loc) as file_:
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for line in file_:
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line = line.strip()
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if line.startswith('#'):
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continue
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if not line:
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continue
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pieces = line.split()
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chunk = pieces.pop(0)
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assert chunk not in seen, chunk
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seen.add(chunk)
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entries.append((chunk, list(pieces)))
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if chunk[0].isalpha() and chunk[0].islower():
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chunk = chunk[0].title() + chunk[1:]
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pieces[0] = pieces[0][0].title() + pieces[0][1:]
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seen.add(chunk)
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entries.append((chunk, pieces))
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return entries
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def read_detoken_rules(lang): # Deprecated?
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loc = path.join(DATA_DIR, lang, 'detokenize')
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entries = []
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with utf8open(loc) as file_:
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for line in file_:
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entries.append(line.strip())
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return entries
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def align_tokens(ref, indices): # Deprecated, surely?
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start = 0
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queue = list(indices)
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for token in ref:
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end = start + len(token)
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emit = []
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while queue and queue[0][1] <= end:
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emit.append(queue.pop(0))
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yield token, emit
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start = end
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assert not queue
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def detokenize(token_rules, words): # Deprecated?
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"""To align with treebanks, return a list of "chunks", where a chunk is a
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sequence of tokens that are separated by whitespace in actual strings. Each
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chunk should be a tuple of token indices, e.g.
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>>> detokenize(["ca<SEP>n't", '<SEP>!'], ["I", "ca", "n't", "!"])
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[(0,), (1, 2, 3)]
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"""
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string = ' '.join(words)
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for subtoks in token_rules:
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# Algorithmically this is dumb, but writing a little list-based match
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# machine? Ain't nobody got time for that.
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string = string.replace(subtoks.replace('<SEP>', ' '), subtoks)
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positions = []
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i = 0
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for chunk in string.split():
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subtoks = chunk.split('<SEP>')
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positions.append(tuple(range(i, i+len(subtoks))))
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i += len(subtoks)
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return positions
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