NER | software implements several experiments | Machine Learning library

 by   synalp Java Version: Current License: No License

kandi X-RAY | NER Summary

kandi X-RAY | NER Summary

NER is a Java library typically used in Artificial Intelligence, Machine Learning applications. NER has no bugs, it has no vulnerabilities and it has low support. However NER build file is not available. You can download it from GitHub.

This software implements several experiments by using the following toolkits. In addition it access external ressources, namely a database with the wikipedia pages until Feb 2014. We also implemented a weakly supervised algorithm that is first initialized with the weights given by the stanford linear classifier trained on little data.
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            kandi-support Support

              NER has a low active ecosystem.
              It has 140 star(s) with 132 fork(s). There are 16 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 2 open issues and 0 have been closed. On average issues are closed in 611 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of NER is current.

            kandi-Quality Quality

              NER has no bugs reported.

            kandi-Security Security

              NER has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              NER does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
              OutlinedDot
              Without a license, all rights are reserved, and you cannot use the library in your applications.

            kandi-Reuse Reuse

              NER releases are not available. You will need to build from source code and install.
              NER has no build file. You will be need to create the build yourself to build the component from source.

            Top functions reviewed by kandi - BETA

            kandi has reviewed NER and discovered the below as its top functions. This is intended to give you an instant insight into NER implemented functionality, and help decide if they suit your requirements.
            • Builds a set with sample distribution
            • Makes a sample of th
            • Returns distance from S to S
            • Generate label prop graph
            • Serialize words
            • Unsupervised classifier
            • Gets the scores for all instances with 0 label 0
            • Main entry point
            • Transforms a DistSimSimulation
            • Process an ASR output file
            • Sets the pattern
            • Check RNumInt
            • Trains theacle scores
            • Generate a random score
            • Save a corpus s source file
            • Evaluate and eval tests
            • Save the NN nodes
            • Implements the parallel coordinate descent algorithm
            • Trains 1 gauss for 1 Gaussian
            • Main entry point for the CoLL compiler
            • Trains the viterbi algorithm
            • Uses the WK classifier for the given class
            • Generate RERE files
            • Draws a single classifier on the given class
            • Trains the classifier n - upper classifierN
            • Visualizes the WK constraint grpc coordgrator
            Get all kandi verified functions for this library.

            NER Key Features

            No Key Features are available at this moment for NER.

            NER Examples and Code Snippets

            No Code Snippets are available at this moment for NER.

            Community Discussions

            QUESTION

            How to generate Precision, Recall and F-score in Named Entity Recognition using Spacy v3? Seeking ents_p, ents_r, ents_f for a small custom NER model
            Asked 2022-Mar-24 at 04:34

            The example code is given below, you may add one or more entities in this example for training purposes (You may also use a blank model with small examples for demonstration). I am seeking a complete working solution for custom NER model evaluation (precision, recall, f-score), Thanks in advance to all NLP experts.

            ...

            ANSWER

            Answered 2022-Mar-24 at 04:34

            I will give a brief example :

            Source https://stackoverflow.com/questions/71593295

            QUESTION

            Error while loading vector from Glove in Spacy
            Asked 2022-Mar-17 at 16:39

            I am facing the following attribute error when loading glove model:

            Code used to load model:

            ...

            ANSWER

            Answered 2022-Mar-17 at 14:08

            spacy version: 3.1.4 does not have the feature from_glove.

            I was able to use nlp.vocab.vectors.from_glove() in spacy version: 2.2.4.

            If you want, you can change your spacy version by using:

            !pip install spacy==2.2.4 on your Jupyter cell.

            Source https://stackoverflow.com/questions/71512064

            QUESTION

            Cannot fix error Types of parameters 'r' and 'a' are incompatible
            Asked 2022-Mar-16 at 01:49

            I try to replicate this code from this blog, but running into some pretty obscure errors

            ...

            ANSWER

            Answered 2022-Mar-16 at 01:49

            I don't know how this code could have worked, since the result of sequenceS has a constraint >>, which cannot be met by an interface due to possible declaration merging (see this TypeScript issue, for example). And that constraint was already present in 2019, which is way before the blog post was written.

            Anyhow, you can get the example to work by declaring OrderHKD as a type rather than an interface:

            Source https://stackoverflow.com/questions/71490052

            QUESTION

            Infinite retry in spring kafka consumer @retryabletopic
            Asked 2022-Mar-14 at 12:24

            I am using @RetryableTopic to implement retry logic in kafka consumer. I gave config as below:

            ...

            ANSWER

            Answered 2022-Feb-28 at 16:32

            It seems there are two separate problems.

            One is that you seem to already have records in the topics, and if you have it configured to earliest the app will read all those records when it starts up. You can either set ConsumerConfig.AUTO_OFFSET_RESET_CONFIG to latest, or, if you're running locally on docker, you can stop the Kafka container and prune the volumes with something like docker system prune --volumes (note that this will erase data from all your stopped containers - use wisely).

            Can you try one of these and test again?

            The other problem is that the framework is wrongly setting the default maxDelay of 30s even though the annotation states the default is to ignore. I'll open an issue for that and add the link here.

            For now You can set a maxDelay such as @Backoff(delay = 600000, multiplier = 3.0, maxDelay = 5400000), then the application should have the correct delays for 10, 30 and 90 minutes as you wanted.

            Let me know if that works out for you, or if you have any other problems related to this issue.

            EDIT: Issue opened, you can follow the development there https://github.com/spring-projects/spring-kafka/issues/2137

            It should be fixed in the next release.

            EDIT 2: Actually the phrasing in the @BackOff annotation is rather ambiguous, but seems like the behavior is correct and you should explicitly set a larger maxDelay.

            The documentation should clarify this behavior in the next release.

            EDIT 3: To answer your question in the comments, the way retryable topics work is the partition is paused for the duration of the delay, but the consumer keeps polling the broker, so longer delays don't trigger rebalancing.

            From your logs the rebalancing is from the main topic's partitions, so it's unlikely it has anything to do with this feature.

            EDIT 4: The Retryable Topics feature was released in Spring for Apache Kafka 2.7.0, which uses kafka-clients 2.7.0. However, there have been several improvements to the feature, so I recommend using the latest Spring Kafka version (currently 2.8.3) if possible to benefit from those.

            Source https://stackoverflow.com/questions/71233946

            QUESTION

            Spacy tokenization add extra white space for dates with hyphen separator when I manually build the Doc
            Asked 2022-Feb-15 at 12:39

            I've been trying to solve a problem with the spacy Tokenizer for a while, without any success. Also, I'm not sure if it's a problem with the tokenizer or some other part of the pipeline.

            Any help is welcome!

            Description

            I have an application that for reasons besides the point, creates a spacy Doc from the spacy vocab and the list of tokens from a string (see code below). Note that while this is not the simplest and most common way to do this, according to spacy doc this can be done.

            However, when I create a Doc for a text that contains compound words or dates with hyphen as a separator, the behavior I am getting is not what I expected.

            ...

            ANSWER

            Answered 2022-Feb-14 at 21:06

            QUESTION

            After installing scrubadub_spacy package, spacy.load("en_core_web_sm") not working OSError: [E053] Could not read config.cfg
            Asked 2022-Feb-06 at 04:46

            I am getting the below error when I'm trying to run the following line of code to load en_core_web_sm in the Azure Machine Learning instance.

            I debugged the issue and found out that once I install scrubadub_spacy, that seems is the issue causing the error.

            ...

            ANSWER

            Answered 2022-Feb-06 at 04:46

            Taking the path from your error message:

            Source https://stackoverflow.com/questions/70976353

            QUESTION

            Show NER Spacy Data in dataframe
            Asked 2022-Jan-25 at 21:27

            I am doing some web scraping to export text info from an html and using a NER (Spacy) to identify information such as Assets Under Management, Addresses, and founding dates of companies. Once the information is extracted, I would like to place it in a dataframe.

            I am working with the following script:

            ...

            ANSWER

            Answered 2022-Jan-25 at 21:27

            After you obtained the body with plain text, you can parse the text into a document and get a list of all entities with their labels and texts, and then instantiate a Pandas dataframe with those data:

            Source https://stackoverflow.com/questions/70855135

            QUESTION

            How to get a description for each Spacy NER entity?
            Asked 2022-Jan-24 at 16:01

            I am using Spacy NER model to extract from a text, some named entities relevant to my problem, such us DATE, TIME, GPE among others.

            For example, I need to recognize the Time Zone in the following sentence:

            ...

            ANSWER

            Answered 2022-Jan-24 at 16:01

            Most labels have definitions you can access using spacy.explain(label).

            For NORP: "Nationalities or religious or political groups"

            For more details you would need to look into the annotation guidelines for the resources listed in the model documentation under https://spacy.io/models/.

            Source https://stackoverflow.com/questions/70835924

            QUESTION

            How to resume training in spacy transformers for NER
            Asked 2022-Jan-20 at 07:21

            I have created a spacy transformer model for named entity recognition. Last time I trained till it reached 90% accuracy and I also have a model-best directory from where I can load my trained model for predictions. But now I have some more data samples and I wish to resume training this spacy transformer. I saw that we can do it by changing the config.cfg but clueless about 'what to change?'

            This is my config.cfg after running python -m spacy init fill-config ./base_config.cfg ./config.cfg:

            ...

            ANSWER

            Answered 2022-Jan-20 at 07:21

            The vectors setting is not related to the transformer or what you're trying to do.

            In the new config, you want to use the source option to load the components from the existing pipeline. You would modify the [component] blocks to contain only the source setting and no other settings:

            Source https://stackoverflow.com/questions/70772641

            QUESTION

            Do I need to do any text cleaning for Spacy NER?
            Asked 2021-Dec-28 at 11:42

            I am new to NER and Spacy. Trying to figure out what, if any, text cleaning needs to be done. Seems like some examples I've found trim the leading and trailing whitespace and then muck with the start/stop indexes. I saw one example where the guy did a bunch of cleaning and his accuracy was really bad because all the indexes were messed up.

            Just to clarify, the dataset was annotated with DataTurks, so you get json like this:

            ...

            ANSWER

            Answered 2021-Dec-28 at 05:19

            First, spaCy does no transformation of the input - it takes it literally as-is and preserves the format. So you don't lose any information when you provide text to spaCy.

            That said, input to spaCy with the pretrained pipelines will work best if it is in natural sentences with no weird punctuation, like a newspaper article, because that's what spaCy's training data looks like.

            To that end, you should remove meaningless white space (like newlines, leading and trailing spaces) or formatting characters (maybe a line of ----?), but that's about all the cleanup you have to do. The spaCy training data won't have bullets, so they might get some weird results, but I would leave them in to start. (Also, bullets are obviously printable characters - maybe you mean non-ASCII?)

            I have no idea what you mean by "muck with the indexes", but for some older NLP methods it was common to do more extensive preprocessing, like removing stop words and lowercasing everything. Doing that will make things worse with spaCy because it uses the information you are removing for clues, just like a human reader would.

            Note that you can train your own models, in which case they'll learn about the kind of text you show them. In that case you can get rid of preprocessing entirely, though for actually meaningless things like newlines / leading and following spaces you might as well remove them anyway.

            To address your new info briefly...

            Yes, character indexes for NER labels must be updated if you do preprocessing. If they aren't updated they aren't usable.

            It looks like you're trying to extract "skills" from a resume. That has many bullet point lists. The spaCy training data is newspaper articles, which don't contain any lists like that, so it's hard to say what the right thing to do is. I don't think the bullets matter much, but you can try removing or not removing them.

            What about stuff like lowercasing, stop words, lemmatizing, etc?

            I already addressed this, but do not do this. This was historically common practice for NLP models, but for modern neural models, including spaCy, it is actively unhelpful.

            Source https://stackoverflow.com/questions/70502457

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

            Vulnerabilities

            No vulnerabilities reported

            Install NER

            You can download it from GitHub.
            You can use NER like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the NER component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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