I want to train a LayoutLM through huggingface transformer, however I need help in creating the training data for LayoutLM from my pdf documents.
Multi page Document Classification can be effectively done by SequenceClassifiers. So here, is a strategy:
Convert Your PDF pages into images and make directory for each different category.
Iterate through all images and create a csv with image Path and label.
Then define your important features and encode the dataset.
Save it in your disk.
Load it back when you need it using Load_from_disk and dataloader.
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I just finished training a Custom Azure Translate Model with a set of 10.000 sentences. I now have the options to review the result and test the data. While I already get a good result score I would like to continue training the same model with additional data sets before publishing. I cant find any information regarding this in the documentation.
The only remotely close option I can see is to duplicate the first model and add the new data sets but this would create a new model and not advance the original one.
Once the project is created, we can train with different models on different datasets. Once the dataset is uploaded and the model was trained, we cannot modify the content of the dataset or upgrade it.
https://learn.microsoft.com/en-us/azure/cognitive-services/translator/custom-translator/quickstart-build-deploy-custom-model
The above document can help you.
I asked this question to better understand some of the nuances between training Spacy models with DocBins serialized to disk, versus loading Example instances via custom data loading function. The goal was to train a Spacy NER model with more data that can fit into RAM (or at least some way to avoid loading the entire file into RAM). Though the custom data loader seemed like one specific way to accomplish this, I am writing this question to ask more generally:
How can one train a Spacy model without loading the entire training data set file during training?
Your only options are using a custom data loader or setting max_epochs = -1. See the docs.
FitBERT is an useful package , but I have a small doubt on BERT development for masked word prediction as below: I trained a bert model with custom corpus using Google's Scripts like create_pretraining_data.py, run_pretraining.py, extract_features.py etc..as a result I got vocab file, .tfrecord file, .json file and check point files.
Now how to use those file for your package to predict a masked word in a given sentence??
From the tensorflow documentation:
A TFRecord file stores your data as a sequence of binary strings. This means you need to specify the structure of your data before you write it to the file. Tensorflow provides two components for this purpose: tf.train.Example and tf.train.SequenceExample. You have to store each sample of your data in one of these structures, then serialize it and use a tf.python_io.TFRecordWriter to write it to disk.
This document along with the tensorflow documentation explain quite well how to use those file types.
While instead to use FitBERT directly through the library you can follow the examples you find on the project's github.
I have trying to develop machine learning based image classification system using Scikit-Learn. I am trying to do is multi class classification. the biggest problem i am facing with Scikit-Learn is how to load the data. Then I came across one of the examples face_recognition.py. which using fetch_lfw_people to fetch data from internet. I could see this example actually does multi class classification. I was trying to find some documentation on the example but was unable to find. I have some question here, what does fetch_lfw_people do ? what does this function load in the lfw_people. Also what i saw in the data folder there are some text file .is the code reading the text files/? My main intention is to load my set of image data but i am unable to do it with fetch_lfw_people in case i change the path that my image folder by data_home and funneled=False.I get erros, I hope i get some answers here
First thing first. You can't directly give images as an input to your classifier. You have to extract some features from you images. Or you can load your image using opencv and use the numpy array as an input to your classifier.
I would suggest you to read some basics of image classification , like how you can train your classifier and all.
Coming to your question about fetch_lfw_people function. It might be downloading already pre-processed image data from any text file. If you are training from your images you have to first convert your image data to some numerical features.
I have a model "en-ner-organization.bin" which I downloaded from apache web-site. It's works fine, but I prefer to train it with my organizations database to increase recognition quality. But after I trained "en-ner-organization.bin" with my organization database - the size of model became less that it was. So it seems, it was overwritten with my data.
I see that there is no possibility to re-train existing model, but maybe there is a way to merge models?
If no - I guess I can add my train data into the .train file of original model, so generated model will consists of default data, plus my data from db. But I can't find such file in web.
So, the main question is: how to keep existing model data and add new data into model?
Thanks
As far as I know it's not possibile to merge different models, but it's possible to specify different files to the finder.
From the sinopsys:
$ bin/opennlp TokenNameFinder
Usage: opennlp TokenNameFinder model1 model2 ... modelN < sentences