text stringlengths 1 93.6k |
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for dataset in ('K&H+N', 'BLESS', 'ROOT09', 'EVALution'):
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for part in ('train', 'val', 'test'):
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with open(os.path.join(dataset, part + '.tsv')) as f:
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reader = csv.reader(f, delimiter='\t', quoting=csv.QUOTE_NONE)
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for row in reader:
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hyponym, hypernym, relation = row[0], row[1], row[2]
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if hyponym not in w2v or hypernym not in w2v:
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continue
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# (K&H+N, BLESS, ROOT09, EVALution)
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if relation in ('hypo', 'hyper', 'HYPER', 'IsA') and hypernym not in positives_trusted[hyponym]:
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positives_trusted[hyponym].append(hypernym)
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elif relation in ('coord', 'Synonym'):
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if hypernym not in negatives[hyponym]:
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negatives[hyponym].append(hypernym)
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if hyponym not in negatives[hypernym]:
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negatives[hypernym].append(hyponym)
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positives_untrusted = defaultdict(lambda: list())
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with open('en_ps59g-rnk3-min100-nomwe-39k.csv') as f:
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reader = csv.reader(f, delimiter='\t', quoting=csv.QUOTE_NONE)
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for row in reader:
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hyponym, hypernym, frequency = row[0], row[1], float(row[2])
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if hyponym in w2v and hypernym in w2v and hypernym not in positives_untrusted[hyponym]:
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positives_untrusted[hyponym].append(hypernym)
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keys_trusted = [k for k in positives_trusted.keys() if len(positives_trusted[k]) > 0]
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trusted_train, trusted_validation_test = train_test_split(np.arange(len(keys_trusted), dtype='int32'), test_size=.4,
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random_state=RANDOM_SEED)
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trusted_validation, trusted_test = train_test_split(trusted_validation_test, test_size=.5, random_state=RANDOM_SEED)
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hypernyms_train = {k: positives_trusted[k] for i in trusted_train for k in (keys_trusted[i],)}
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for hyponym, hypernyms in positives_untrusted.items():
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if hyponym in hypernyms_train:
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for hypernym in hypernyms:
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if not hypernym in hypernyms_train[hyponym]:
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hypernyms_train[hyponym].append(hypernym)
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hypernyms_validation = {k: positives_trusted[k] for i in trusted_validation for k in (keys_trusted[i],)}
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hypernyms_test = {k: positives_trusted[k] for i in trusted_test for k in (keys_trusted[i],)}
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subsumptions_train = [(x, y) for x, ys in hypernyms_train.items() for y in ys]
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subsumptions_validation = [(x, y) for x, ys in hypernyms_validation.items() for y in ys]
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subsumptions_test = [(x, y) for x, ys in hypernyms_test.items() for y in ys]
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def write_subsumptions(subsumptions, filename):
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with open(filename, 'w', newline='') as f:
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writer = csv.writer(f, dialect='excel-tab', lineterminator='\n')
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for pair in subsumptions:
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writer.writerow(pair)
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write_subsumptions(subsumptions_train, 'subsumptions-train.txt')
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write_subsumptions(subsumptions_validation, 'subsumptions-validation.txt')
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write_subsumptions(subsumptions_test, 'subsumptions-test.txt')
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with open('synonyms.txt', 'w', newline='') as f:
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writer = csv.writer(f, dialect='excel-tab', lineterminator='\n')
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for word, words in negatives.items():
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writer.writerow((word, ','.join(words)))
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# <FILESEP>
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"""
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Description:
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Notes:
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Requirements:
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pip install python-multipart
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"""
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import json
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from loguru import logger
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from asr.paraformer import ALIASR
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from fastapi.responses import JSONResponse
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from fastapi import FastAPI, File, UploadFile
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from contextlib import asynccontextmanager
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auto_asr: ALIASR = None # 全局变量
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@asynccontextmanager
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async def lifespan(app: FastAPI):
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"""程序启动前加载模型"""
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global auto_asr
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auto_asr = ALIASR()
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yield
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"""销毁模型"""
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auto_asr = None
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app = FastAPI(lifespan=lifespan)
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# 根目录访问的处理
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@app.get("/")
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async def read_root():
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return json.dumps({"code": 0, "msg": "欢迎访问ASR", "data": ""})
|
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