This commit is contained in:
Mikołaj Pokrywka
parent
c7d96f1597
commit
c0894d950a
10687
dev-0/out.tsv
10687
dev-0/out.tsv
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import torch
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from transformers import GPT2Tokenizer, GPT2LMHeadModel, AutoModelForCausalLM
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import sys
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import regex as re
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import sys
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tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
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model = AutoModelForCausalLM.from_pretrained('gpt2')
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for line in sys.stdin:
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input_text = line.split('\t')[-2].rstrip()
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input_ids = tokenizer.encode(input_text, return_tensors='pt')
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with torch.no_grad():
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outputs = model(input_ids)
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token_logits = outputs.logits[:, -1, : ]
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probs = torch.nn.functional.softmax(token_logits, dim=1)[0]
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top = torch.topk(probs, 10)
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top_indices = top.indices.tolist()
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top_probs = top.values.tolist()
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top_words = [tokenizer.decode(x) for x in top_indices]
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string_to_print = ''
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sum_probs = 0
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for w, p in zip(top_words, top_probs):
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if '<unk>' in w:
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continue
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if re.search(r'\p{L}+', w):
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string_to_print += f"{w}:{p} "
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sum_probs += p
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if string_to_print == '':
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print(f"the:0.2 a:0.3 :0.5")
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continue
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unknow_prob = 1 - sum_probs
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string_to_print += f":{unknow_prob}"
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print(string_to_print)
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153
run.py
153
run.py
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@ -1,153 +0,0 @@
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import lzma
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import matplotlib.pyplot as plt
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from math import log
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from collections import OrderedDict
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from collections import Counter
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import regex as re
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from itertools import islice
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def freq_list(g, top=None):
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c = Counter(g)
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if top is None:
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items = c.items()
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else:
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items = c.most_common(top)
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return OrderedDict(sorted(items, key=lambda t: -t[1]))
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def get_words(t):
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for m in re.finditer(r'[\p{L}0-9-\*]+', t):
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yield m.group(0)
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def ngrams(iter, size):
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ngram = []
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for item in iter:
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ngram.append(item)
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if len(ngram) == size:
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yield tuple(ngram)
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ngram = ngram[1:]
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PREFIX_TRAIN = 'train'
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words = []
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counter_lines = 0
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with lzma.open(f'{PREFIX_TRAIN}/in.tsv.xz', 'r') as train, open(f'{PREFIX_TRAIN}/expected.tsv', 'r') as expected:
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for t_line, e_line in zip(train, expected):
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t_line = t_line.decode("utf-8")
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t_line = t_line.rstrip()
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e_line = e_line.rstrip()
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t_line_splitted_by_tab = t_line.split('\t')
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t_line_cleared = t_line_splitted_by_tab[-2] + ' ' + e_line + ' ' + t_line_splitted_by_tab[-1]
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words += t_line_cleared.split()
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counter_lines+=1
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if counter_lines > 90000:
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break
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# lzmaFile = lzma.open('dev-0/in.tsv.xz', 'rb')
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# content = lzmaFile.read().decode("utf-8")
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# words = get_words(trainset)
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ngrams_ = ngrams(words, 2)
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def create_probabilities_bigrams(w_c, b_c):
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probabilities_bigrams = {}
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for bigram, bigram_amount in b_c.items():
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if bigram_amount <=2:
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continue
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p_word_before = bigram_amount / w_c[bigram[0]]
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p_word_after = bigram_amount / w_c[bigram[1]]
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probabilities_bigrams[bigram] = (p_word_before, p_word_after)
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return probabilities_bigrams
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words_c = Counter(words)
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word_=''
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bigram_c = Counter(ngrams_)
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ngrams_=''
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probabilities = create_probabilities_bigrams(words_c, bigram_c)
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items = probabilities.items()
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probabilities = OrderedDict(sorted(items, key=lambda t:t[1], reverse=True))
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items=''
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# sorted_by_freq = freq_list(ngrams)
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PREFIX_VALID = 'test-A'
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def count_probabilities(w_b, w_a, probs, w_c, b_c):
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results_before = {}
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results_after = {}
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for bigram, probses in probs.items():
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if len(results_before) > 20 or len(results_after) > 20:
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break
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if w_b == bigram[0]:
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results_before[bigram] = probses[0]
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if w_a == bigram[1]:
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results_after[bigram] = probses[1]
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a=1
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best_ = {}
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for bigram, probses in results_before.items():
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for bigram_2, probses_2 in results_after.items():
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best_[bigram[1]] = probses * probses_2
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for bigram, probses in results_after.items():
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for bigram_2, probses_2 in results_before.items():
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if bigram[0] in best_:
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if probses * probses_2 < probses_2:
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continue
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best_[bigram[0]] = probses * probses_2
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items = best_.items()
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return OrderedDict(sorted(items, key=lambda t:t[1], reverse=True))
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with lzma.open(f'{PREFIX_VALID}/in.tsv.xz', 'r') as train:
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for t_line in train:
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t_line = t_line.decode("utf-8")
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t_line = t_line.rstrip()
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t_line = t_line.replace('\\n', ' ')
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t_line_splitted_by_tab = t_line.split('\t')
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words_pre = t_line_splitted_by_tab[-2].split()
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words_po = t_line_splitted_by_tab[-1].split()
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w_pre = words_pre[-1]
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w_po = words_po[0]
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probs_ordered = count_probabilities(w_pre, w_po,probabilities, words_c, bigram_c)
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if len(probs_ordered) ==0:
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print(f"the:0.5 a:0.3 :0.2")
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continue
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result_string = ''
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counter_ = 0
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for word_, p in probs_ordered.items():
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if counter_>4:
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break
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re_ = re.search(r'\p{L}+', word_)
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if re_:
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word_cleared = re_.group(0)
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result_string += f"{word_cleared}:{str(p)} "
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else:
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if result_string == '':
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result_string = f"the:0.5 a:0.3 "
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continue
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counter_+=1
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result_string += ':0.1'
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print(result_string)
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a=1
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