Closed sophieball closed 4 years ago
Update: the technique I put above doesn't work on GH data - it uses unigram and logistic regression - took too long and the program was killed
No model passed to Forecaster. Initializing default forecaster model: Cumulative Bag-of-words...
Initializing default unigram CountVectorizer...
Initializing default classification model (standard scaled logistic regression
Trying this: https://github.com/CornellNLP/Cornell-Conversational-Analysis-Toolkit/tree/master/examples/conversations-gone-awry
This one also looks interesting: https://github.com/CornellNLP/Cornell-Conversational-Analysis-Toolkit/blob/master/examples/hyperconvo/demo_new.ipynb
Update July 27:
Update Aug 4: Talked to one of the authors. Confirmed that we've been on the right track. Forgot to ask how to label prompts because we ran out of time.
@CaptainEmerson Can you run code from PR #72, and copy the bad_conver.log
. It should contain something like this. Most are comments, so you don't need to share the doc. Useful information (also less sensible) is things like
0 1 2 3 4 5 type_id
do>* 0.675650 0.977704 1.012123 1.033751 1.003250 0.974516 0.0
do>you 0.698812 1.096976 1.074800 1.029700 1.031715 0.970195 0.0
On your last day, we looked over this file. I have also set it aside on my desktop, should we need it later. I'll close this issue, but feel free to reopen if it's not done.
Predicting Conversations Gone Awry With Convokit: https://github.com/CornellNLP/Cornell-Conversational-Analysis-Toolkit/blob/master/convokit/forecaster/tests/cumulativeBoW_demo.ipynb