BERT-E2E-ABSA | Exploiting BERT for End-to-End Aspect | Predictive Analytics library
kandi X-RAY | BERT-E2E-ABSA Summary
kandi X-RAY | BERT-E2E-ABSA Summary
Exploiting BERT for End-to-End Aspect-based Sentiment Analysis (W-NUT@EMNLP 2019)
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Top functions reviewed by kandi - BETA
- Compute the log likelihood
- Calculate the log likelihood of the model
- Compute the log of a tensor
- Compute the joint likelihood
- Train the model
- Performs viterbi tags
- Evaluate the model
- Perform viterbi decoding
- Convert examples to features
- Truncate a sequence pair
- Load weights from a tensorflow model
- Builds a mapping from TF to pytorch model
- Run prediction
- Convert BTS tags to TOCs
- Convert TIO tags to BIO tags
- Convert a Ts tag sequence into a list of Ts
BERT-E2E-ABSA Key Features
BERT-E2E-ABSA Examples and Code Snippets
Community Discussions
Trending Discussions on Predictive Analytics
QUESTION
GPU is good for parallel computing but the problem is some machine learning libraries don't utilize the GPU, unless that machine learning based on image processing or some sort of graphics processing, what if I am using machine learning for predictive Analytics? do libraries like TensorFlow utilize the GPU? or they use only CPU? or can I choose which processing unit to use? whats the deal here?
note: predictive Analysis requires no graphics processing.
...ANSWER
Answered 2020-Nov-21 at 21:35The computation that happens in the GPU in any of the machine learning frameworks that support GPUs is not limited to graphical processing. For instance, if your model is a simple logistic regression, a framework such as TensorFlow will run it on the GPU if properly configured.
The advantage of GPUs for machine learning is that training big neural networks benefits greatly from the high level of parallelism that the GPUs offer.
If you want to know more about this, I'd recommend you start here or here.
some things to consider:- how much a model will benefit from running in the GPU will depend on how much it will benefit from parallel computation in general.
- Deep Learning models can be applied to predictive analytics, as well as more classical machine learning models. Bear in mind that neural nets are possibly the category of models that will benefit inherently from the GPU (see links above).
- Even though running models using GPUs (or even more specialised hardware) can bring benefits, I would suggest that you don't choose a framework and, especially, don't choose an algorithm based solely on the fact that it will benefit from parallelism, but rather look at how appropriate a given algorithm is for the data you have.
QUESTION
I have a pandas dataframe which is a large number of answers given by users in response to a survey and I need to re-structure it. There are up to 105 questions asked each year, but I only need maybe 20 of them.
The current structure is as below.
What I want to do is re-structure it so that the row values become column names and the answer given by the user is then the value in that column. In a picture (from Excel), what I want is the below (I know I'll need to re-name my columns, but that's fine once I can create the structure in the first place):
Is it possible to re-structure my dataframe this way? The outcome of this is to use some predictive analytics to predict a target variable, so I need to re-strcture before I can use Random Forest, kNN, and so on.
...ANSWER
Answered 2020-Nov-01 at 19:39You might want try pivoting your table:
QUESTION
I have js files Dashboard and Adverts. I managed to get Dashboard to list the information in one json file (advertisers), but when clicking on an advertiser I want it to navigate to a separate page that will display some data (Say title and text) from the second json file (productadverts). I can't get it to work. Below is the code for the Dashboard and next for Adverts. Then the json files
...ANSWER
Answered 2020-May-17 at 23:55The new object to get params in React Navigation 5 is:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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No vulnerabilities reported
Install BERT-E2E-ABSA
The valid tagging strategies/schemes (i.e., the ways representing text or entity span) in this project are BIEOS (also called BIOES or BMES), BIO (also called IOB2) and OT (also called IO). If you are not familiar with these terms, I strongly recommend you to read the following materials before running the program: a. Inside–outside–beginning (tagging). b. Representing Text Chunks. c. The paper associated with this project.
Reproduce the results on Restaurant and Laptop dataset: # train the model with 5 different seed numbers python fast_run.py
Train the model on other ABSA dataset: place data files in the directory ./data/[YOUR_DATASET_NAME] (please note that you need to re-organize your data files so that it can be directly adapted to this project, following the input format of ./data/laptop14/train.txt should be OK). set TASK_NAME in train.sh as [YOUR_DATASET_NAME]. train the model: sh train.sh
(** New feature **) Perform pure inference/direct transfer over test/unseen data using the trained ABSA model: place data file in the directory ./data/[YOUR_EVAL_DATASET_NAME]. set TASK_NAME in work.sh as [YOUR_EVAL_DATASET_NAME] set ABSA_HOME in work.sh as [HOME_DIRECTORY_OF_PRETRAINED_ABSA_MODEL] run: sh work.sh
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