spacy | 易学教程

Is possible to keep spacy in memory to reduce the load time? [closed]

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问题 Closed . This question needs details or clarity. It is not currently accepting answers. Want to improve this question? Add details and clarify the problem by editing this post. Closed 2 years ago . I want to use spacy as for NLP for an online service. Each time a user makes a request I call the script "my_script.py" which starts with: from spacy.en import English nlp = English() The problem I'm having is that those two lines take over 10 seconds, is it possible to keep English() in the ram or

SpaCy: how to load Google news word2vec vectors?

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问题 I've tried several methods of loading the google news word2vec vectors (https://code.google.com/archive/p/word2vec/): en_nlp = spacy.load('en',vector=False) en_nlp.vocab.load_vectors_from_bin_loc('GoogleNews-vectors-negative300.bin') The above gives: MemoryError: Error assigning 18446744072820359357 bytes I've also tried with the .gz packed vectors; or by loading and saving them with gensim to a new format: from gensim.models.word2vec import Word2Vec model = Word2Vec.load_word2vec_format(

SpaCy: how to load Google news word2vec vectors?

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Python Cannot install module spaCy

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问题 I´m new to python and I ran into a problem I can´t solve. I would like to install and use the package spacy in python. Therefore I opened cmd and ran pip install spacy While installing the dependecies I get an error message: ---------------------------------------- Command ""c:\users\xxx\appdata\local\programs\python\python37\python.exe" -u -c "import setuptools, tokenize; file ='C:\Users\xxx\AppData\Local\Temp\pip-install-6vcdnb_4\numpy\setup.py';f=getattr(tokenize, 'open', open)( file )

自然语言分类任务 (1)

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文章目录自然语言分类任务数据集模型准备数据数据集预览查看一个样本切分train/val 创建vocabulary 查看训练集中最常见的单词查看单词表查看label 创建iterators Word Averaging模型模型结构模型配置模型参数计算初始化参数定义优化器和损失函数计算预测的准确率模型训练train 模型验证evaluate 读取保存的模型语句测试 RNN模型（没有跑）模型结构模型配置词向量使用glove训练好的词向量添加PAD单词、UNK单词模型参数计算模型训练读取保存的模型语句测试 CNN模型单卷积核模型结构模型配置词向量使用glove训练好的词向量添加PAD单词、UNK单词模型参数计算优化器和损失函数模型训练读取保存的模型语句测试卷积核并联模型结构模型配置词向量使用glove训练好的词向量添加PAD单词、UNK单词模型参数计算模型训练 Word Averaging (mask) 数据准备创建vocabulary 创建iterators Word Averaging模型模型结构模型配置计算模型参数初始化模型优化器和损失函数计算预测的准确率模型训练模型验证自然语言分类任务使用Pytorch模型和TorchText做情感分类（检测一段文字的情感是正面情绪

Spacy to Conll format without using Spacy's sentence splitter

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问题 This post shows how to get dependencies of a block of text in Conll format with Spacy's taggers. This is the solution posted: import spacy nlp_en = spacy.load('en') doc = nlp_en(u'Bob bought the pizza to Alice') for sent in doc.sents: for i, word in enumerate(sent): if word.head == word: head_idx = 0 else: head_idx = word.head.i - sent[0].i + 1 print("%d\t%s\t%s\t%s\t%s\t%s\t%s"%( i+1, # There's a word.i attr that's position in *doc* word, word.lemma_, word.tag_, # Fine-grained tag word.ent

Using nlp.pipe() with pre-segmented and pre-tokenized text with spaCy

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问题 I am trying to tag and parse text that has already been split up in sentences and has already been tokenized. As an example: sents = [['I', 'like', 'cookies', '.'], ['Do', 'you', '?']] The fastest approach to process batches of text is .pipe() . However, it is not clear to me how I can use that with pre-tokenized, and pre-segmented text. Performance is key here. I tried the following, but that threw an error docs = [nlp.tokenizer.tokens_from_list(sentence) for sentence in sents] nlp.tagger

How to use Hindi Model in RASA NLU?

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问题 I have build my model for Hindi language using FastText with spacy backend. I followed this tutorial to to build my model using FastText. This URL I have also linked my model with spacy by following command python -m spacy link nl_model hi Model is linked successfully you can check in the image below Now I am not finding any help for using hindi language, Like what kind of config files do I need to use, where to import hindi model and how to proceed now? I also have question like how our data

calculate all the metrics of a custom Named Entity recognition (NER)Model using Spacy and ner.manual

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问题 i have made a spacy (2.1.8) model which works on some labels like data, time, coordinate,stars... now I want to see all the metrics related to each entity using spacy. something like this precision recall f1-score support B-LOC 0.810 0.784 0.797 1084 I-LOC 0.690 0.637 0.662 325 B-MISC 0.731 0.569 0.640 339 I-MISC 0.699 0.589 0.639 557 B-ORG 0.807 0.832 0.820 1400 I-ORG 0.852 0.786 0.818 1104 B-PER 0.850 0.884 0.867 735 I-PER 0.893 0.943 0.917 634 I have noticed that I can use Scorer for that:

Spacy EN Model issue

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问题 Need to know the difference between spaCy's en and en_core_web_sm model. I am trying to do NER with Spacy.( For Organization name) Please find bellow the script I am using import spacy nlp = spacy.load("en_core_web_sm") text = "But Google is starting from behind. The company made a late push \ into hardware, and Apple’s Siri, available on iPhones, and Amazon’s \ Alexa software, which runs on its Echo and Dot devices, have clear leads in consumer adoption." doc = nlp(text) for ent in doc.ents: