spacy-course | 👩‍🏫 Advanced NLP with spaCy : A free online course | Natural Language Processing library

 by   ines Python Version: Current License: MIT

kandi X-RAY | spacy-course Summary

kandi X-RAY | spacy-course Summary

spacy-course is a Python library typically used in Artificial Intelligence, Natural Language Processing applications. spacy-course has no vulnerabilities, it has a Permissive License and it has medium support. However spacy-course has 28 bugs and it build file is not available. You can download it from GitHub.

‍ Advanced NLP with spaCy: A free online course

            kandi-support Support

              spacy-course has a medium active ecosystem.
              It has 1902 star(s) with 322 fork(s). There are 53 watchers for this library.
              It had no major release in the last 6 months.
              There are 5 open issues and 29 have been closed. On average issues are closed in 34 days. There are 1 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of spacy-course is current.

            kandi-Quality Quality

              spacy-course has 28 bugs (0 blocker, 0 critical, 28 major, 0 minor) and 100 code smells.

            kandi-Security Security

              spacy-course has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              spacy-course code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              spacy-course is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              spacy-course releases are not available. You will need to build from source code and install.
              spacy-course has no build file. You will be need to create the build yourself to build the component from source.
              Installation instructions, examples and code snippets are available.
              spacy-course saves you 6667 person hours of effort in developing the same functionality from scratch.
              It has 13842 lines of code, 476 functions and 1090 files.
              It has low code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed spacy-course and discovered the below as its top functions. This is intended to give you an instant insight into spacy-course implemented functionality, and help decide if they suit your requirements.
            • Liefert die Anattendert .
            • Get the length component of the document .
            • Extracts a country component from a document .
            • Returns the Wikipedia page URL for the span .
            • Test if doc has token number .
            • Erzeugt die Tag - Tag .
            • Returns the reversed version of the token .
            Get all kandi verified functions for this library.

            spacy-course Key Features

            👩‍🏫 Advanced NLP with spaCy: A free online course

            spacy-course Examples and Code Snippets

            Parsing to CoNLL with spaCy, spacy-stanza, and spacy-udpipe,Usage,In Python
            Pythondot img1Lines of Code : 63dot img1License : Permissive (BSD-2-Clause)
            copy iconCopy
            import spacy_conll
            nlp = 
            nlp.add_pipe("conll_formatter", last=True)
            def init_parser(
                model_or_lang: str,
                parser: str,
                is_tokenized: bool = False,
                disable_sbd: bool = False,
                parser_opts: Optional[Dict] = None,
            Parsing to CoNLL with spaCy, spacy-stanza, and spacy-udpipe,Installation
            Pythondot img2Lines of Code : 10dot img2License : Permissive (BSD-2-Clause)
            copy iconCopy
            # only includes spacy by default
            pip install spacy_conll
            # include spacy-stanza and spacy-udpipe
            pip install spacy_conll[parsers]
            # include pandas
            pip install spacy_conll[pd]
            # include pandas, spacy-stanza and spacy-udpipe
            pip install spacy_conll[al  
            PHPdot img3Lines of Code : 5dot img3no licencesLicense : No License
            copy iconCopy
            Bare Minimum:

            Community Discussions


            number of matches for keywords in specified categories
            Asked 2022-Apr-14 at 13:32

            For a large scale text analysis problem, I have a data frame containing words that fall into different categories, and a data frame containing a column with strings and (empty) counting columns for each category. I now want to take each individual string, check which of the defined words appear, and count them within the appropriate category.

            As a simplified example, given the two data frames below, i want to count how many of each animal type appear in the text cell.



            Answered 2022-Apr-14 at 13:32

            Here's a way do to it in the tidyverse. First look at whether strings in df_texts$text contain animals, then count them and sum by text and type.



            Apple's Natural Language API returns unexpected results
            Asked 2022-Apr-01 at 20:30

            I'm trying to figure out why Apple's Natural Language API returns unexpected results.

            What am I doing wrong? Is it a grammar issue?

            I have the following four strings, and I want to extract each word's "stem form."



            Answered 2022-Apr-01 at 20:30

            As for why the tagger doesn't find "accredit" from "accreditation", this is because the scheme .lemma finds the lemma of words, not actually the stems. See the difference between stem and lemma on Wikipedia.

            The stem is the part of the word that never changes even when morphologically inflected; a lemma is the base form of the word. For example, from "produced", the lemma is "produce", but the stem is "produc-". This is because there are words such as production and producing In linguistic analysis, the stem is defined more generally as the analyzed base form from which all inflected forms can be formed.

            The documentation uses the word "stem", but I do think that the lemma is what is intended here, and getting "accreditation" is the expected behaviour. See the Usage section of the Wikipedia article for "Word stem" for more info. The lemma is the dictionary form of a word, and "accreditation" has a dictionary entry, whereas something like "accredited" doesn't. Whatever you call these things, the point is that there are two distinct concepts, and the tagger gets you one of them, but you are expecting the other one.

            As for why the order of the words matters, this is because the tagger tries to analyse your words as "natural language", rather than each one individually. Naturally, word order matters. If you use .lexicalClass, you'll see that it thinks the third word in text2 is an adjective, which explains why it doesn't think its dictionary form is "accredit", because adjectives don't conjugate like that. Note that accredited is an adjective in the dictionary. So "is it a grammar issue?" Exactly.



            Tokenize text but keep compund hyphenated words together
            Asked 2022-Mar-29 at 09:16

            I am trying to clean up text using a pre-processing function. I want to remove all non-alpha characters such as punctuation and digits, but I would like to retain compound words that use a dash without splitting them (e.g. pre-tender, pre-construction).



            Answered 2022-Mar-29 at 09:14

            To remove all non-alpha characters but - between letters, you can use



            Create new boolean fields based on specific bigrams appearing in a tokenized pandas dataframe
            Asked 2022-Feb-16 at 20:47

            Looping over a list of bigrams to search for, I need to create a boolean field for each bigram according to whether or not it is present in a tokenized pandas series. And I'd appreciate an upvote if you think this is a good question!

            List of bigrams:



            Answered 2022-Feb-16 at 20:28

            You could use a regex and extractall:



            ModuleNotFoundError: No module named 'milvus'
            Asked 2022-Feb-15 at 19:23

            Goal: to run this Auto Labelling Notebook on AWS SageMaker Jupyter Labs.

            Kernels tried: conda_pytorch_p36, conda_python3, conda_amazonei_mxnet_p27.



            Answered 2022-Feb-03 at 09:29

            I would recommend to downgrade your milvus version to a version before the 2.0 release just a week ago. Here is a discussion on that topic:



            Which model/technique to use for specific sentence extraction?
            Asked 2022-Feb-08 at 18:35

            I have a dataset of tens of thousands of dialogues / conversations between a customer and customer support. These dialogues, which could be forum posts, or long-winded email conversations, have been hand-annotated to highlight the sentence containing the customers problem. For example:

            Dear agent, I am writing to you because I have a very annoying problem with my washing machine. I bought it three weeks ago and was very happy with it. However, this morning the door does not lock properly. Please help

            Dear customer.... etc

            The highlighted sentence would be:

            However, this morning the door does not lock properly.

            1. What approaches can I take to model this, so that in future I can automatically extract the customers problem? The domain of the datasets are broad, but within the hardware space, so it could be appliances, gadgets, machinery etc.
            2. What is this type of problem called? I thought this might be called "intent recognition", but most guides seem to refer to multiclass classification. The sentence either is or isn't the customers problem. I considered analysing each sentence and performing binary classification, but I'd like to explore options that take into account the context of the rest of the conversation if possible.
            3. What resources are available to research how to implement this in Python (using tensorflow or pytorch)

            I found a model on HuggingFace which has been pre-trained with customer dialogues, and have read the research paper, so I was considering fine-tuning this as a starting point, but I only have experience with text (multiclass/multilabel) classification when it comes to transformers.



            Answered 2022-Feb-07 at 10:21

            This type of problem where you want to extract the customer problem from the original text is called Extractive Summarization and this type of task is solved by Sequence2Sequence models.

            The main reason for this type of model being called Sequence2Sequence is because the input and the output of this model would both be text.

            I recommend you to use a transformers model called Pegasus which has been pre-trained to predict a masked text, but its main application is to be fine-tuned for text summarization (extractive or abstractive).

            This Pegasus model is listed on Transformers library, which provides you with a simple but powerful way of fine-tuning transformers with custom datasets. I think this notebook will be extremely useful as guidance and for understanding how to fine-tune this Pegasus model.



            Assigning True/False if a token is present in a data-frame
            Asked 2022-Jan-06 at 12:38

            My current data-frame is:



            Answered 2022-Jan-06 at 12:13


            How to calculate perplexity of a sentence using huggingface masked language models?
            Asked 2021-Dec-25 at 21:51

            I have several masked language models (mainly Bert, Roberta, Albert, Electra). I also have a dataset of sentences. How can I get the perplexity of each sentence?

            From the huggingface documentation here they mentioned that perplexity "is not well defined for masked language models like BERT", though I still see people somehow calculate it.

            For example in this SO question they calculated it using the function



            Answered 2021-Dec-25 at 21:51

            There is a paper Masked Language Model Scoring that explores pseudo-perplexity from masked language models and shows that pseudo-perplexity, while not being theoretically well justified, still performs well for comparing "naturalness" of texts.

            As for the code, your snippet is perfectly correct but for one detail: in recent implementations of Huggingface BERT, masked_lm_labels are renamed to simply labels, to make interfaces of various models more compatible. I have also replaced the hard-coded 103 with the generic tokenizer.mask_token_id. So the snippet below should work:



            Mapping values from a dictionary's list to a string in Python
            Asked 2021-Dec-21 at 16:45

            I am working on some sentence formation like this:



            Answered 2021-Dec-12 at 17:53

            You can first replace the dictionary keys in sentence to {} so that you can easily format a string in loop. Then you can use itertools.product to create the Cartesian product of dictionary.values(), so you can simply loop over it to create your desired sentences.



            What are differences between AutoModelForSequenceClassification vs AutoModel
            Asked 2021-Dec-05 at 09:07

            We can create a model from AutoModel(TFAutoModel) function:



            Answered 2021-Dec-05 at 09:07

            The difference between AutoModel and AutoModelForSequenceClassification model is that AutoModelForSequenceClassification has a classification head on top of the model outputs which can be easily trained with the base model


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install spacy-course

            The requirements.txt in the repository defines the packages that are installed when building it with Binder. For this course, I'm using the source repo as the Binder repo, as it allows to keep everything in one place. It also lets the exercises reference and load other files (e.g. JSON), which will be copied over into the Python environment. I build the binder from a branch binder, though, which I only update if Binder-relevant files change. Otherwise, every update to master would trigger an image rebuild. You can specify the binder settings like repo, branch and kernel type in the "juniper" section of the meta.json. I'd recommend running the very first build via the interface on the Binder website, as this gives you a detailed build log and feedback on whether everything worked as expected. Enter your repository URL, click "launch" and wait for it to install the dependencies and build the image.


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