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Completed all the steps and I don't know how to interpret the results #2
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Each row in this file is about a personality trait (EXT, NEU, AGR, CON, and OPN). The 10 first columns show the accuracy of 10 fold cross-validation and the last column shows the average accuracy in 10 folds. If it is helpful for you, you can make us happy by giving our project a star. :D |
Hi @python-ai-13! bert-serving-start -model_dir uncased_L-12_H-768_A-12/ -num_worker=4 -max_seq_len=NONE -show_tokens_to_client -pooling_layer -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 and my computer crashes instantly, and I need to reboot it manually. It crashes when WORKER-1 tries to load a graph. Maybe I don't have enough memory or other requirement to run the code... Please, do you have any suggestions? |
Hi @di-press; bert-serving-start -model_dir uncased_L-12_H-768_A-12/ -num_worker=1 -max_seq_len=NONE -show_tokens_to_client -pooling_layer -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 |
Thanks, @saminfatehir ! I'll try :) |
Hi, I'm in the same boat as python-ai13. If I understood the whole process well, the personality traits extracted are from the essay_200_max_split.csv (that itself comes from essays_original_splitted.csv), with each row inside this file corresponding to a different essay, written by different users. However, the final output (that is "svm_0_layer_with_mairesse_rev200_.csv") only yield the accuracy of predicting well the personality of the users of the original dataset, but do not tell us any information concerning their personality (is user X possess the Extraversion type or else, that sort of thing)? Is there a Python script that would do such a thing? |
Hi, Since this is a supervised learning model, we evaluate our model on test data and report the accuracy. If you want to know the personality of a new text (base on our model), you can use predictor.py (which was recently added). |
I can see a new file called "svm_0_layer_with_mairesse_rev200_.csv" with a bunch of numbers in it... if that's the results how do I read it?
Sorry, I'm new with python.
Thanks
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