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.
Related
I have a dxf file that I have uploaded to oss and translated it to svf.
How can I fetch the layer states in my 2D model using forgeviewer?
In autocad, I have these layer states Screenshot for autocad layer states.
Namely:
F1 Component Plan
F2 Electrical Plan
F3 Bracket Plan
But when in the forgeviewer, I cant find those lawyer states (Grouping).
I'm afraid this type of information may not be available. Generally, the Forge Model Derivative service always tries to extract a "balanced" amount of information from design files (enough for previewing purposes, but not too much, to make sure the output keeps a reasonable size).
When you load your DXF file into the viewer, you can try querying its metadata using viewer.model.getData(), and see if you find the layer state there. I did try that with one of my testing DXFs, and didn't see this information there.
Finally, if you really do need the layer state data, there's another option - you could use Design Automation for AutoCAD, with a custom AutoCAD plugin that would extract all the information you need directly from the source file.
We are now in process of evaluating integration solutions and comparing Mule and Boomi.
Use case is to read an Excel file, map the columns to a pre-defined set of JSON attributes and then use the JSON to insert records into a database. The mapping may vary from one Excel template to another wherein the column names in an Excel may be different from others.
How do I inject mapping information (source vs target) from outside integration flow?
Note: In Mule, I'm able to do that using a mapping variable (value is JSON) that I inject using Mule DataWeave language.
Boomi's mapping component is static in terms of structure but more versatile solutions are certainly possible.
The data processor component opens up Groovy, JavaScript, and XSLT 3.0 as options. These are Turing-complete languages that can be used to bend Boomi to almost any outcome.
You could make the Boomi UI available to those who need to write the maps in JSON. It's a pretty simple interface to learn. By using a route component, there could be one "parent" process that governs the a process for each template/process and then a map for each template. Such a solution would be pretty easy to build and run; allowing the template-specific processes to be deployed independently of the "parent".
You could map to a generic columnar structure and then dynamically alter the target
columns by writing a SQL procedure that would alter the target columns.
I've come across attempts to do what you're describing (not using either Boomi or Mulesoft) which were tragic failures: https://www.zdnet.com/article/uk-rural-payments-agency-rpa-it-failure-and-gross-incompetence-screws-farmers/ I draw your attention to the NAO's points:
ensure the system specifications retain a realistic level of flexibility
and
bespoke software is costly to develop, needs to be thoroughly tested, and takes more time to implement
The general goal for such a requirement like yours is usually to make transformation/ETL available to "non-programmers" which denies the reality that there are many more skills to delivering an outcome than "programming".
I was curious if it is possible to use transfer learning in text generation, and re-train/pre-train it on a specific kind of text.
For example, having a pre-trained BERT model and a small corpus of medical (or any "type") text, make a language model that is able to generate medical text. The assumption is that you do not have a huge amount of "medical texts" and that is why you have to use transfer learning.
Putting it as a pipeline, I would describe this as:
Using a pre-trained BERT tokenizer.
Obtaining new tokens from my new text and adding them to the existing pre-trained language model (i.e., vanilla BERT).
Re-training the pre-trained BERT model on the custom corpus with the combined tokenizer.
Generating text that resembles the text within the small custom corpus.
Does this sound familiar? Is it possible with hugging-face?
I have not heard of the pipeline you just mentioned. In order to construct an LM for your use-case, you have basically two options:
Further training BERT (-base/-large) model on your own corpus. This process is called domain-adaption as also described in this recent paper. This will adapt the learned parameters of BERT model to your specific domain (Bio/Medical text). Nonetheless, for this setting, you will need quite a large corpus to help BERT model better update its parameters.
Using a pre-trained language model that is pre-trained on a large amount of domain-specific text either from the scratch or fine-tuned on vanilla BERT model. As you might know, the vanilla BERT model released by Google has been trained on Wikipedia and BookCorpus text. After the vanilla BERT, researchers have tried to train the BERT architecture on other domains besides the initial data collections. You may be able to use these pre-trained models which have a deep understanding of domain-specific language. For your case, there are some models such as: BioBERT, BlueBERT, and SciBERT.
Is it possible with hugging-face?
I am not sure if huggingface developers have developed a robust approach for pre-training BERT model on custom corpora as claimed their code is still in progress, but if you are interested in doing this step, I suggest using Google research's bert code which has been written in Tensorflow and is totally robust (released by BERT's authors). In their readme and under Pre-training with BERT section, the exact process has been declared. This will provide you with Tensorflow checkpoint, which can be easily converted to Pytorch checkpoint if you'd like to work with Pytorch/Transformers.
It is entirely possible to both pre-train and further pre-train BERT (or almost any other model that is available in the huggingface library).
Regarding the tokenizer - if you are pre-training on a a small custom corpus (and therefore using a trained bert checkpoint), then you have to use the tokenizer that was used to train Bert. Otherwise, you will just confuse the model.
If your use case is text generation (from some initial sentence/part of sentence), then I can advise you to check gpt-2 (https://huggingface.co/gpt2). I haven't used GPT-2, but with some basic research I think you can do:
from transformers import GPT2Tokenizer, TFGPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = TFGPT2Model.from_pretrained('gpt2')
and follow this tutorial: https://towardsdatascience.com/train-gpt-2-in-your-own-language-fc6ad4d60171 on how to train the gpt-2 model.
Note: I am not sure if DeBERTa-V3, for example, can be pre-trained as usual. I have checked their github repo and it seems that for V3 there is no official pre-training code (https://github.com/microsoft/DeBERTa/issues/71). However, I think that using huggingface we can actually do it. Once I have time I will run a pre-training script and verify this.
I'm aware that there are python and powershell methods to convert plain text files, csv's etc.... into json format for upload into NoSQL DBs such as CouchDB.
But according to the CouchDB definitive guide, it makes it seems like there is a native built in way of doing this kind of conversion, without the need for a 3rd party tool.
This older thread appears to hint at this:
Filter and update functions in CouchDB?
This part in particular:
There are other design document functions that are being introduced at the >time of this writing, including _update and _filter that we aren’t covering in >depth here. Filter functions are covered in Chapter 20, Change Notifications. >Imagine a web service that POSTs an XML blob at a URL of your choosing when >particular events occur. PayPal’s instant payment notification is one of >these. With an _update handler, you can POST these directly in CouchDB and it >can parse the XML into a JSON document and save it. The same goes for CSV, >multi-part form, or any other format.
But when I dig deeper I don't find anything concrete.
The supporting wiki link is not clear to me (a beginner with json/NoSQL/curl stuff: http://wiki.apache.org/couchdb/Document_Update_Handlers
Hopefully this is a simple yes/no. And any links to help on this that is better than the above link also appreciated, thank you!
CouchDB supports transforming the internal documents/views into many other formats through the use of show and list functions. It's not a "native" transformation, as you define the transformation yourself, it's not magical.
That being said, there is not a similar mechanism for the reverse (ie: converting some arbitrary format into JSON documents) but you're much better off scripting that with a full-featured language/script and using the bulk docs API to do your imports in batch.
I was consulting several references to discover how I may output trained Weka models into Java source code so that I may use the classifiers I am training in actual code for research applications I have been developing.
As I was playing with Weka 3.7, I noticed that while it does output Java code to its main text buffer when use simpler classification (supervised in my case this time) methods such as J48 decision tree, it removes the option (rather, it voids it by removing the ability to checkmark it and fades the text) to output Java code for RandomTree and RandomForest (which are the ones that give me the best performance in my situation).
Note: I am clicking on the "More Options" button and checking "Output source code:".
Does Weka not allow you to output RandomTree or RandomForest as Java code? If so, why? Or if it does and just doesn't put it in the output buffer (since RF is multiple decision trees which I imagine it doesn't want to waste buffer space), how does one go digging up where in the file system Weka outputs java code by default?
Are there any tricks to get Weka to give me my trained RandomForest as Java code? Or is Serialization of the output *.model files my only hope when it comes to RF and RandomTree?
Thanks in advance to those who provide help.
NOTE: (As an addendum to the answer provided below) If you run across a similar situation (requiring you to use your trained classifier/ML model in your code), I recommend following the links posted in the answer that was provided in response to my question. If you do not specifically need the Java code for the RandomForest, as an example, de-serializing the model works quite nicely and fits into Java application code, fulfilling its task as a trained model/hardened algorithm meant to predict future unlabelled instances.
RandomTree and RandomForest can't be output as Java code. I'm not sure for the reasoning why, but they don't implement the "Sourceable" interface.
This explains a little about outputting a classifier as Java code: Link 1
This shows which classifiers can be output as Java code: Link 2
Unfortunately I think the easiest route will be Serialization, although, you could maybe try implementing "Sourceable" for other classifiers on your own.
Another, but perhaps inconvenient solution, would be to use Weka to build the classifier every time you use it. You wouldn't need to load the ".model" file, but you would need to load your training data and relearn the model. Here is a starters guide to building classifiers in your own java code http://weka.wikispaces.com/Use+WEKA+in+your+Java+code.
Solved the problem for myself by turning the output of WEKA's -printTrees option of the RandomForest classifier into Java source code.
http://pielot.org/2015/06/exporting-randomforest-models-to-java-source-code/
Since I am using classifiers with Android, all of the existing options had disadvantages:
shipping Android apps with serialized models didn't reliably work across devices
computing the model on the phone took too much resources
The final code will consist of three classes only: the class with the generated model + two classes to make the classification work.