I'm trying to build an entity recognition model using Spacy and that links any recognized set of entities to documents in the database.
My holdup for now is that I haven't found a good way of integrating the Sentiment analysis I'm to use with my model.
Do I really need a sentiment analysis?
All suggestions are welcome!!!
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A general question. I have used i.e. Weka classifier model functionallity in their tool. But is there a way to "call Weka" and get a model in response from a website?
It is not important that it is Weka, but I want to implement some simple classification based on a json coming from a web-site.
Thanks.
You can write a REST webservice in Java which loads your model and makes predictions using data it receives, sending back the predictions in a suitable format. There are a number of frameworks for writing such webservices (e.g., JAX-RS).
In terms of using the Weka API, check out the Use Weka in your Java code article.
How to use named entity recognition using Deep Learning? I want to build a model using DL for named entity recognition.
There are many pre-trained models/library for Named Entity Recognition(NER), you can use HuggingFace pre-traied modes, SpaCy and NLTK for the same.
If you want to go deep dive and train a Deep Learning model from scratch, you shall explore about BERT.
Also, I would recommend to go through Kaggle notebooks about Named Entity Recognition.
I am trying to build a key-points detection model for human, as there are many pretrained networks available to generate key-points, but i want to practice myself to create a keypoint detection model with custom dataset, cant find anything in web if someone have some info's then please share.
I want more points specified to the human body, but to do so i need to create a custom model to generate such kind of key-points in human body, i checked some annotation tools but those annotation tool helps to adjust the points they have already specified when taking dataset like COCO etc, i think we cant add more points to the image. i just want to build a new model with custom key-points.
please share your views about my view on to the problem and please suggest some links if you have any idea about the same
I have created a detailed github repo Custom Keypoint Detection for dataset preparation, model training and inference on Centernet-hourglass104 keypoint detection model based on Tensorflow Object detection API with examples.
This could help you in training your keypoint detection model on custom dataset.
Any issues related to the project can be raised in the github itself and doubts can be cleared here.
There seems to be any amount of RDF-format for the OSLC but what I'm looking for is a simple E-R-like view of the OSLC metamodel which shows the concepts and relationships which can be used to understand the organisation and possible queries.
Is there a (graphic) representation of the OSLC metamodel anywhere?
If you are after a simple graphical diagram, you can find UML models under this Lyo-Docs
repo. you can find the source .emx files, as well as .png snapshots under the folder "OSLC-V2/images/".
I you are developing OSLC applications, you might want to consider the modelling tool Lyo Designer.
There, you can find a graphical model of the OSLC Core and Domain concepts. The models are based on a OSLC-specific modelling language. Lyo Designer allows you define/extend your own models, from which you can generate an OSLC application, based on the Eclipse Lyo SDK.
I here assume you are aware of the java class implementations of the OSLC Core concepts in Eclipse Lyo. There is also an implementation of the domain specifications.
I want to train the basic translation system with only a glossary.
The language pair is ENtoKO. I trained 1,700 sentences in the dictionary tab in the manner described in the article.
I did not select anything in the "Training" tab.
https://cognitive.uservoice.com/knowledgebase/articles/1166938-hub-building-a-custom-system-using-a-dictionary-o
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However, expectation and system did not translate the terms. and unlike the document (Microsoft Translator Hub User Guide.pdf), the training completes much time.
Dictionary only training: You can now train a custom translation system when with just a dictionary and no other parallel documents. There is no minimum size for that dictionary, one entry is enough. Just upload the dictionary, which is an Excel file with the language identifier as column header, include it in your training set, and hit train. The training completes very quickly, then you can deploy and use your system with that dictionary. The dictionary applies the translation you provided with 100% probability, regardless of context. This type of training does not produce bleu score and this option only available if MS models are available for given language pair.
Why this training only losing silp Dicionary would like to know. If the update is a feature that is not the intended schedule?
In addition, I am wondering if there is a plan to introduce the Dictionary application function to the NMT Api function as well.
Customizing NMT is available now by using Custom Translator (Preview) and we expect the Dictionary feature to be available when Custom Translator is Generally Available.
You do need to be using the Microsoft Translator Text API v3 and Custom Translator supports language pairs where NMT languages are available today (Korean is a NMT language).
Thank you.
Yes.
You can customize our en-ko general domain baseline with your dictionary. Please follow our quick start documentation.