text_classifier | Natural Language Processing library
kandi X-RAY | text_classifier Summary
kandi X-RAY | text_classifier Summary
该项目是使用TextCNN/TextRNN/TextRCNN的文本分类任务，嵌入层可接入Word2Vec，Bert，也可以直接使用词粒度的随机embedding，带有Attention模块，项目基于Tensorflow2.3开发。数据的获取见app_comments_spider爬虫项目。
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QUESTION
I'm trying to learn how to use some ML stuff for Android. I got the Text Classification demo working and seems to work fine. So then I tried creating my own model.
The code I used to create my own model was this:
...ANSWER
Answered 2021-May-27 at 15:50In your codes you trained a MobileBERT model, but saved to the path of average_word_vec? spec = model_spec.get('mobilebert_classifier') model.export(export_dir='average_word_vec')
One posssiblity is: you use the model of average_word_vec, but add MobileBERT metadata, thus the preprocessing doesn't match.
Could you follow the Model Maker tutorial and try again? https://colab.sandbox.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/tutorials/model_maker_text_classification.ipynb Make sure change the export path.
QUESTION
So I used Bert model trained it and saved it as hdf5 file, but when I try to predict , it shows this error :
IndexError: list index out of range
here is the code
...ANSWER
Answered 2021-May-18 at 01:44As shown in the ktrain tutorials and example notebooks like this one, you need to use the Predictor instance to make predictions on raw text inputs:
QUESTION
I'm trying to build a form in HTML/Tailwind CSS/ReactJS. I have created/styled the form fine, but I seem to be having issues where the file input is not properly being centered. It appears that the element has some inherent width, but it won't center itself within that space.
I've gone ahead and created a CodePen to try and represent this issue: https://codepen.io/developerryan/pen/mdREJXo
or you can view this segment here:
...ANSWER
Answered 2021-Apr-09 at 19:27Editing the input value, in this case, is something that is usually restricted for security reasons. You can always mimic the style you want yourself though.
Written example here for your consideration:
QUESTION
I am using Mlflow as a work orchestration tool. I have a Machine Learning pipeline. In this pipeline, I have real-time data. I'm listening this data with Apache Kafka. Also, I'm doing this: Whenever 250 message comes to this topic, I'm gathering them, and I'm appending this message my previous data. After that, my training function is triggered. Thus, I am able to making new training in every 250 new data. With Mlflow, I can show the results, metrics and any other parameters of trained models. But After training occurred one time, the second one doesn't occurs, and It throws me this error which I have shown in title. Here it is my consumer:
...ANSWER
Answered 2021-Feb-26 at 14:45I think you need an MLflow "run" for every new batch of data, so that your parameters are logged independently for each new training.
So, try the following in your consumer:
QUESTION
Trying to use text classifier model shared by https://github.com/allenai/scibert/blob/master/scibert/models/text_classifier.py
Everything used to work and suddenly I keep getting this error: Cannot register text_classifier as Model; name already in use for TextClassifier
What might be the reason? any suggestion?
...ANSWER
Answered 2021-Feb-17 at 13:55The name is already taken. Something that’s already a part of AllenNLP uses that name already, so you need to pick a different one.
For the curious, AllenNLP creates a registry of models, so that you can select a model at the command line. (That’s what the decorator is doing.) This requires the names to be unique.
The name text_classifier was used by AllenNLP only after the external package you’re using used it. It worked in May 2019, when that file was last updated. But 17 months ago, AllenNLP started using it. So it’s not your fault; it’s a mismatch between those two packages (at least, in their current versions).
QUESTION
I trained a model by using Naive Bayes. I have high accuracy, but now I want to give a sentence then I want to see it's sentiment. Here it is my code:
...ANSWER
Answered 2021-Feb-15 at 13:49First, put the preprocessing in a function:
QUESTION
I have trained a BERT model using ktrain (tensorflow wrapper) to recognize emotion on text, it works but it suffers from really slow inference. That makes my model not suitable for a production environment. I have done some research and it seems pruning could help.
Tensorflow provides some options for pruning e.g. tf.contrib.model_pruning. The problem is that it is not a not a widely used technique and I can not find a simple enough example that could help me to understand how to use it. Can someone help?
I provide my working code below for reference.
...ANSWER
Answered 2020-Oct-20 at 17:52The distilbert model in ktrain is created using Hugging Face transformers, which means you can use that library to prune the model. See this link for more information and the example script. You may need to convert the model to PyTorch before using the script (in addition to making some modifications to the script itself). The approach is based on the paper Are Sixteen Heads Really Better Than One?.
QUESTION
I am trying to fit BERT text classifier. My training and test data looks as follows.
...ANSWER
Answered 2020-Sep-10 at 11:58My personal idea is that when you instantiate the learner with ktrain.get_learner you give it a batch size = 6 as input parameter.
So when you try to train the learner by simply doing learner.fit_onecycle (2e-5, 1), it takes exactly one batch for training, in fact 4500 training data / batch size (6) = 750 data to train on.
At this point either try to change the batch size, or do a for loop like this:
QUESTION
How can we use a different pretrained model for the text classifier in the ktrain library? When using:
model = text.text_classifier('bert', (x_train, y_train) , preproc=preproc)
This uses the multilangual pretrained model
However, I want to try out a monolingual model as well. Namely the Dutch one: ''wietsedv/bert-base-dutch-cased', which is also used in other k-train implementations, for example.
However, when trying to use this command in the text classifier it does not work:
...ANSWER
Answered 2020-Sep-03 at 22:09There are two text classification APIs in ktrain. The first is the text_classifier API which can be used for a select number of both transformers and non-transformers models. The second is the Transformer API which can be used with any transformers model including the one you listed.
The latter is explained in detail in this tutorial notebook and this medium article.
For instance, you can replace MODEL_NAME with any model you want in the example below:
Example:
QUESTION
I have the following code:
...ANSWER
Answered 2020-Jan-23 at 21:23You are looking for the zip function.
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Install text_classifier
You can use text_classifier 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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