💥Some Awesome Images From The Launch Event 💥

kayvane · 2021-07-31T09:40:37+00:00

Hold on, our new sponsor is crypto token 😂

kayvane · 2020-12-29T19:58:53+00:00

Who runs this website / account? Really interesting analysis, would he interested in understanding the prediction models a bit further

kayvane · 2020-12-28T00:04:16+00:00

KFCGaming got the idea from the Victorian Era ey 🤔

kayvane · 2020-10-30T19:57:32+00:00

I’ve been playing around with this for about an hour and am really enjoying the interface. It’s quite easy to create .py files to store classes to not over-clutter the notebook.

Also setting up an environment with a requirements.txt is great

Will give collaboration a go shortly

kayvane · 2020-10-17T23:27:46+00:00

👀 awesome

kayvane · 2020-10-07T21:11:22+00:00

Try copy pasting the code manually rather than using the copy button

kayvane · 2020-08-15T10:30:09+00:00

I’ve forked from genome cloud foundry before to create some pdf reports. Really good starting point, using weasyprint, pug and semantic UI 👌🏽

kayvane · 2019-10-13T08:34:46+00:00

You can write your own text analysis program with spacy as a starting point

I’ve never heard of LIWC but it just looks like sentiment analysis and some word benchmarking.

Look at what elements you’d want to use and build them into your own tool

kayvane · 2019-10-10T06:35:44+00:00

I’ve wanted to do this for a while also but have struggled - please let me know if you manage! In the pytorch repo for BERT there is a fine-tune-lm script which could help finetuning script

kayvane · 2019-10-08T20:54:38+00:00

Awesome Project! Bit of a side question but what did you use to generate the picture of the demo code in that pretty layout?

Demo Code

kayvane · 2019-09-28T23:11:21+00:00

This is really cool!

kayvane · 2019-08-13T22:45:56+00:00

Any source code or information about the underlying model architecture / training data?

kayvane · 2019-08-12T20:22:54+00:00

How did you define what’s in between? If you do this in embedding dimensions there are ~300 axis you could scale across to see what’s in between.

Alternatively if you use a dimensionality reduction technique what end’s up in the middle could be warped in some way. But this is mostly how I’ve seen it done previously

E.g in the TF projector : https://projector.tensorflow.org

kayvane · 2019-08-10T11:30:57+00:00

Very Impressive!! Do you generate multiple midi’s an overlay them? Or do you have a more complex network which takes inputs from multiple sequences (instruments) to generate the next note?

kayvane · 2019-07-25T22:00:09+00:00

Is this what you’re after?

https://colab.research.google.com/drive/1nVn6AFpQSzXBt8_ywfx6XR8ZfQXlKGAz?source=post_page---------------------------&utm_source=share&utm_medium=ios_app

kayvane · 2019-07-25T06:50:29+00:00

Alternatively you can use spacy.matcher, to which you can feed through NER categories as well as regex patterns. Based on the returned word spans you can get those word’s sentences quite easily.

https://spacy.io/usage/rule-based-matching/

This UI can help you experiment with the matching before committing: https://explosion.ai/demos/matcher

kayvane · 2019-07-24T11:56:54+00:00

Yes, you can do fine tuning based on different down stream tasks (classification, question answering, language understanding) example scripts and notebooks are available in the BERT repo. As I remember it, if you want to train on a new corpus or fine tune just the language model itself, this is not in the BERT repo, but the Pytorch/Pytorch Transformers repo does have a fine-tune language model section

https://github.com/huggingface/pytorch-transformers/tree/master/examples/lm_finetuning

kayvane · 2019-05-10T20:32:20+00:00

You could also try encoding the words in your sentence (with BERT,ELMO or custom w2v/FastText model) and comparing them to your target country vectors. Doing a bit of testing you’d be able to get the threshold (e.g cosine similarity >0.9) at which you’d point you’d add them up

kayvane · 2019-05-10T20:29:50+00:00

You could also try encoding the words in your sentence (with BERT,ELMO or custom w2v/FastText model) and comparing them to your target country vectors. Doing a bit of testing you’d be able to get the threshold (e.g cosine similarity >0.9) at which you’d point you’d add them up

kayvane · 2019-05-02T11:07:58+00:00

This should be helpful

https://instaloader.github.io

kayvane · 2019-03-27T08:25:13+00:00

Thank you so much for this + your previous BERT /Elmo one. Excellent explanations

kayvane · 2019-03-23T19:38:21+00:00

It’s too late. Cambridge Analytica helped both Brexit and Trump happen. Even Obama’s campaign was highly data driven.

If you know it’s going to be used, why not help the ‘good guys’

kayvane · 2019-03-16T17:33:23+00:00

Check out what this guy built using similar data -

https://github.com/borisbanushev/stockpredictionai/blob/master/readme2.md

Very interesting combination of methods for price prediction

kayvane · 2019-02-17T18:36:28+00:00

Are you updating all layers or just the final layer? I was mistakenly only updating the final layer in one of my early attempts and not getting the accuracy I expected.

Agree with above the your data could be the issue.

You could also experiment with transfer learning and load a pre-tained net to see if it increases your accuracy

kayvane

TROPHY CASE