vocabulary | Maintained anymore ] Python Module | Natural Language Processing library
kandi X-RAY | vocabulary Summary
kandi X-RAY | vocabulary Summary
[Not Maintained anymore] Python Module to get Meanings, Synonyms and what not for a given word
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Top functions reviewed by kandi - BETA
- Returns a list of alltonyms for the given phrase
- Respond to a given format
- Returns a json object from url
- Get the link to the API
- A context manager
- Translate a phrase
- Parses tuc_content_content into a dictionary
- Clean a dictionary
- Symbolize a phrase
- Get pronunciation
- Get the meanings of a phrase
- Get a usage example
- Get the part of speech
- Get the hyphenation
vocabulary Key Features
vocabulary Examples and Code Snippets
Lines of Code : 117License : Non-SPDX (Apache License 2.0)
Copydef _categorical_column_with_vocabulary_file(key,
vocabulary_file,
vocabulary_size=None,
num_oov_buckets=0,
def _warm_start_var_with_vocab(var,
current_vocab_path,
current_vocab_size,
prev_ckpt,
prev_vocab_path,
def categorical_column_with_vocabulary_file(key,
vocabulary_file,
vocabulary_size=None,
num_oov_buckets=0,
Community Discussions
Trending Discussions on vocabulary
QUESTION
There is a function given as follows
...ANSWER
Answered 2021-Jun-15 at 21:34Your code doesn’t attempt to not fail if w isn’t a key in id2word, so it shouldn’t be too much of a surprise when it does fail. You could try changing
QUESTION
I have a react application (Node back end) running on Heroku (free option) connecting to a MongoDB running on Atlas (also free option). When I connect the application from my local machine to the Atlas DB all is fine and data retrieved (all 108 K records) in about 10 seconds, smaller amounts (4-500 records) of data in much less time. The same request from the application running on Heroku to the Atlas DB fails. The application running on Heroku can retrieve a small number of records (1-10) from the same collection of (108 K records), in less than a second. As soon as I try to retrieve a couple of hundred records the system fails. Below are the logs. I included the section of the logs that show a successful retrieval of 1 record and then failing on the request for about 450 records.
I have three questions:
- What is the cause of the issue?
- Is there a work around in the free option of Heroku?
- If there is no work around in the free option, what Heroku pay level will I need to get to and what steps will I need to take to get this working? I will probably upgrade in the future but want to prove all is working before going in that direction.
Logs:
...ANSWER
Answered 2021-Jun-14 at 18:09You're running out of heap memory in your node server. It might be because there's some statement that uses a lot of memory. You can try to find that or you can try to increase node memory like this.
QUESTION
I'm getting this error Unhandled Rejection (TypeError): state.push is not a function while using redux thunk but while refrshing the page after error, new word is getting added to the DB.
Below is my code.
...ANSWER
Answered 2021-Jun-13 at 17:33The issue is that the first call to get the dictionary mutates the state invariant, from array to object. The JSON response object from "https://vocabulary-app-be.herokuapp.com/dictionary" is an object with message and data keys.
QUESTION
i have a dataframe with the columns title and tokenized words. Now I read in all tokenized words into a list called vcabulary looking like this:
[['hello', 'my', 'friend'], ['jim', 'is', 'cool'], ['peter', 'is', 'nice']]
now I want to go through this list of lists and count every word for every list.
...ANSWER
Answered 2021-Jun-13 at 15:32Convert your 2D list, into a normal list, then use collections.Counter() to return a dictionary of each words occurrence count.
QUESTION
I have recently sourced and curated a lot of reddit data from Google Bigquery.
The dataset looks like this:
Before passing this data to word2vec to create a vocabulary and be trained, it is required that I properly tokenize the 'body_cleaned' column.
I have attempted the tokenization with both manually created functions and NLTK's word_tokenize, but for now I'll keep it focused on using word_tokenize.
Because my dataset is rather large, close to 12 million rows, it is impossible for me to open and perform functions on the dataset in one go. Pandas tries to load everything to RAM and as you can understand it crashes, even on a system with 24GB of ram.
I am facing the following issue:
- When I tokenize the dataset (using NTLK word_tokenize), if I perform the function on the dataset as a whole, it correctly tokenizes and word2vec accepts that input and learns/outputs words correctly in its vocabulary.
- When I tokenize the dataset by first batching the dataframe and iterating through it, the resulting token column is not what word2vec prefers; although word2vec trains its model on the data gathered for over 4 hours, the resulting vocabulary it has learnt consists of single characters in several encodings, as well as emojis - not words.
To troubleshoot this, I created a tiny subset of my data and tried to perform the tokenization on that data in two different ways:
- Knowing that my computer can handle performing the action on the dataset, I simply did:
ANSWER
Answered 2021-May-27 at 18:28First & foremost, beyond a certain size of data, & especially when working with raw text or tokenized text, you probably don't want to be using Pandas dataframes for every interim result.
They add extra overhead & complication that isn't fully 'Pythonic'. This is particularly the case for:
- Python
listobjects where each word is a separate string: once you've tokenized raw strings into this format, as for example to feed such texts to Gensim'sWord2Vecmodel, trying to put those into Pandas just leads to confusing list-representation issues (as with your columns where the same text might be shown as either['yessir', 'shit', 'is', 'real']– which is a true Python list literal – or[yessir, shit, is, real]– which is some other mess likely to break if any tokens have challenging characters). - the raw word-vectors (or later, text-vectors): these are more compact & natural/efficient to work with in raw Numpy arrays than Dataframes
So, by all means, if Pandas helps for loading or other non-text fields, use it there. But then use more fundamntal Python or Numpy datatypes for tokenized text & vectors - perhaps using some field (like a unique ID) in your Dataframe to correlate the two.
Especially for large text corpuses, it's more typical to get away from CSV and instead use large text files, with one text per newline-separated line, and any each line being pre-tokenized so that spaces can be fully trusted as token-separated.
That is: even if your initial text data has more complicated punctuation-sensative tokenization, or other preprocessing that combines/changes/splits other tokens, try to do that just once (especially if it involves costly regexes), writing the results to a single simple text file which then fits the simple rules: read one text per line, split each line only by spaces.
Lots of algorithms, like Gensim's Word2Vec or FastText, can either stream such files directly or via very low-overhead iterable-wrappers - so the text is never completely in memory, only read as needed, repeatedly, for multiple training iterations.
For more details on this efficient way to work with large bodies of text, see this artice: https://rare-technologies.com/data-streaming-in-python-generators-iterators-iterables/
QUESTION
I have a scannerless parser grammar utilizing the CharsAsTokens faux lexer which generates a usable Java Parser class for ANTLR4 versions through 4.6. But when updating to ANTLR 4.7.2 through 4.9.3-SNAPSHOT, the tool generates code producing dozens of compilation errors from the same grammar file, as detailed below.
My question here is simply: Are scannerless parser grammars no longer supported, or must their character-based terminals be specified differently in 4.7 and beyond?
Update:
Unfortunately, I cannot post my complete grammar here as it is derived from FOUO security marking guidance, access to which is retricted by the U.S. government (I am a DoD/IC contractor).
The incompatible upgrade issue however is entirely reproducible with the CSQL.g4 scannerless parser grammar example referred to by Ter in Section 5.6 of The Definitive ANTLR 4 Reference.
As does my grammar, the CSQL example uses CharsAsTokens.java for its tokenizer, and CharVocab.tokens as its token vocabulary.
Note that every token name is specified by its ASCII character-literal equivalent, as in:
...ANSWER
Answered 2021-Jun-07 at 00:17Try defining a GrammarLexer.g4 file instead of the GrammarLexer.tokens file. (You'd still using the options: { tokenVocab = GrammarLexer; } like you do if you create the GrammarLexer.tokens file} It could be as simple as:
QUESTION
I am using pandas in Python and I am trying to transform a dataframe. I have a dataframe like this:
Column 1 Column 2 1 22 1 23 2 34 2 35 2 36 3 49I would like to group the values in the first column while creating a new column/attribute in a different column for the values belonging to grouped values from the first column. I don't know what is the biggest number of values from Column 2 belonging to a unique value in Column 1.
Column 1 Column 2_1 Column 2_2 Column 2_3 1 22 23 None/NaN 2 34 35 36 3 49 None/NaN None/NaNI have been looking for quite a while how to do that efficiently, but I probably lack the vocabulary to find good results. Any help is appreciated.
...ANSWER
Answered 2021-Jun-04 at 12:44TRY:
QUESTION
I have a view that lists blog articles. The blog content type has a taxonomy reference field to the 'tags' vocabulary, authors can select 1 or multiple tags. The view exposes the 'Has taxonomy terms (with depth) (exposed)' filter (as a list of checkboxes) so that users can search for blog articles containing 1 or more tags.
Now, i'm trying to pre-select 1 of the checkboxes that are exposed to the user in the hook_form_FORM_ID_alter() hook. It should be a simple as the code below but it just doesn't work. The tag i'm trying to pre-select has the ID 288.
What am i doing wrong? Thx...
...ANSWER
Answered 2021-Jun-01 at 03:22You have to set user input like this:
QUESTION
Let's assume i have "News" entity which has got ManyToMany "Tag" relation
...ANSWER
Answered 2021-May-31 at 07:31Some things to notice first:
For doctrine annotations it is possible to use the ::class-constant:
QUESTION
I have a three tables: topics, sentences, and vocabulary. Sentences and vocabulary both have a belongsTo topic_id, but not all topics necessarily have both vocabulary and sentences. I want to get a count of all topics that have both sentences and vocabulary.
I have it working if I do one table at a time:
...ANSWER
Answered 2021-May-29 at 22:13One simple method is count(distinct):
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Install vocabulary
You can use vocabulary like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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