I am looking for ways to train my GATE. Not just running the application, but training it with like data that already annotated (not just plain document). I really appreciate if anybody help me. Thanks :)
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I'm using GPT-3 for some experiments where I prompt the language model with tests from cognitive science. The tests have the form of short text snippets. Now I'd like to check whether GPT-3 has already encountered these text snippets during training. Hence my question: Is there any way to sift through GPT-3's training text corpora? Can one find out whether a certain string is part of these text corpora?
Thanks for your help!
I don't think that's possible, unfortunately. GPT-3's training corpora is private.
But if that was possible, it would be great for detecting plagiarism. Maybe ask if it it knows where a certain line of text came from?
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 trying to develop a spam detector application using svm classifier.
But I am not able to find any input data. Can anyone please suggest what kind of input data should I take and from where I could find it. I tried google but didnt found the satisfactory answeres
Stanford machine learning course (ml-class.org) has a lab (no. 6) where you build a spam
filter using support vector machines. The dataset is supplied.
Please suggest a good machine learning classifier for truecasing of dataset.
Also, Is it possible to specify out own rules/features for truecasing in such a classifier? Thanks for all your suggestions.
Thanks
I implemented a version of a truecaser in Python. It can be trained for any language when you provide enough data (i.e. correctly cased sentences).
For English, it achieves an accuracy of 98.38% on sample sentences from Wikipedia. A pre-trained model for English is provided.
You can find it here:
https://github.com/nreimers/truecaser
Please take a look at this whitepaper.
http://www.cs.cmu.edu/~llita/papers/lita.truecasing-acl2003.pdf
They report 98% of accuracy.