I'm sorry, I'm just starting to learn NLP. I am learning how to use Google Flan.
I'm not sure how to provide the few-shot example to the model and run which file to get the results.
Here is the code link:
https://github.com/google-research/FLAN
Here is the paper:
https://arxiv.org/abs/2109.01652
Many thanks!
Related
Good day, I am a student that is interested in NLP. I have come across the demo on AllenNLP's homepage, which stated that:
The model is a simple LSTM using GloVe embeddings that is trained on the binary classification setting of the Stanford Sentiment Treebank. It achieves about 87% accuracy on the test set.
Is there any reference to the sample code or any tutorial that I can follow to replicate this result, so that I can learn more about this subject? I am trying to obtain a Regression Output (Instead of classification).
I hope that someone can point me in the right direction.. Any help is much appreciated. Thank you!
AllenAI provides all code for examples and lib opensource on Git, including AllenNLP.
I found exactly how the example was run here: https://github.com/allenai/allennlp/blob/master/allennlp/tests/data/dataset_readers/stanford_sentiment_tree_bank_test.py
However, to make it a Regression task, you'll have to tweak directly on Pytorch, which is the underlying technology for AllenNLP.
I have a project where I need to analyze a text to extract some information if the user who post this text need help in something or not, I tried to use sentiment analysis but it didn't work as expected, my idea was to get the negative post and extract the main words in the post and suggest to him some articles about that subject, if there is another way that can help me please post it below and thanks.
for the dataset i useed, it was a dataset for sentiment analyze, but now I found that it's not working and I need a dataset use for this subject.
Please use the NLP methods before processing the sentiment analysis. Use the TFIDF, Word2Vector to create vectors on the given dataset. And them try the sentiment analysis. You may also need glove vector for the conducting analysis.
For this topic I found that this field in machine learning is called "Natural Language Questions" it's a field where machine learning models trained to detect questions in text and suggesting answer for them based on data set you are working with, check this article for more detail.
I am doing a License Plate Recognition system using python. I browsed through the net and I found many people have done the recognition of characters in the license plate using kNN algorithm.
Can anyone explain how we predict the characters in the License Plate using kNN ?
Is there any other algorithm or method that can do the prediction better ?
I am referring to this Git repo https://github.com/MicrocontrollersAndMore/OpenCV_3_License_Plate_Recognition_Python
Well, I did this 5 years ago. I will suggest you that, maybe right now is so much better to do this using ML Classifier Models, but if you want to use OpenCV. OpenCV has a pretty cool way to make ANPR using an OCR.
When I did it, I used a RasberryPi for processing and capture images and with c++ run openCV in another computer. I recommend you check this repo and if you're interested look for the book reference there. I hope my answer helps you to find your solution.
https://github.com/MasteringOpenCV/code.
How to use the Inception V3 tensorflow module to train with our own requirement dataset images. Say for example I want to train the Inception V3 module with the different cool drinkcompany brands Pepsi, Sprite etc.. How it can be achieved..??
In the link https://github.com/tensorflow/models/tree/master/inception they have explained with the ImageNet. I am bit confused with that. Please explain the stuff.
I suggest you to check Transfer Learning. which consists in retrain only the last layers with new categories
How to Retrain Inception's Final Layer for New Categories
Baptiste's answer linking to the Tensorflow site is good. This is a very broad question and his link is a good start.
If you'd like something a little more step-by-step then the Tensorflow for Poets tutorial is basically the same but doesn't require the use of Bazel commands. It initially uses flowers but you can use whatever dataset you want.
There are many other examples and tutorials on the web. I found some more with a quick search including this page and this video.
Good Luck!
Could anyone give me an Example in detail which show how SVM exactly work with all the necessary Mathematics?
As I tried to search in the internet and found very little example about SVM.
Thank you.
You can watch the stanford lectures on machine learning from lectures 6 to 8. Its on youtube. It teaches you everything on SVM and the mathematics involved in SVM. Have a look at it..