dinobby / hypemo Goto Github PK
View Code? Open in Web Editor NEWThe official implementation of ACL 2023 paper "Label-Aware Hyperbolic Embeddings for Fine-grained Emotion Classification."
The official implementation of ACL 2023 paper "Label-Aware Hyperbolic Embeddings for Fine-grained Emotion Classification."
Hi! I was wondering which classes/functions should be used to map, for example, a BERT embedding to the hyperbolic space (using the Poincaré ball model). In the paper it is mentioned that you use an exponential map, but I am not sure where this can be found in the code. Is it e.g. line 106 in hypbert.py, or do you need to use PoincareBall Manifold class in poincare.py?
(FYI: I'm not referring to training label embeddings; just the projections)
Thanks!
I am trying to run in my dataset, and found two bugs:
Bug1: show_node_labels is not defined in train_label_embedding.py
Bug2: When running train_label_embedding.py line 36 will cause error, because your config.py will run the last several lines.
Hello sir, just read your paper and I’m really fascinated by your good F1 numbers. However I want to apply your proposed model on the go-emotion original multi label setup. I made the following attempts and modifications:
Weirdly, I ended up with getting some very strange results. Specifically, the Poincaré loss term inside the total loss calculation becomes negative values from the end of the first epoch and onwards. The negative values grows smaller and smaller, ultimately causes the total loss term become negative. I’ve made some simple investigations and found that it’s due to the dot_product term(in Poincaré model compute_metrics() function) value becoming too large.
Can you please suggest which part I did wrong? Should I also change some hyper parameters in the hyperbolic part to resolve negative loss issue? Do you think your model theoretically support multi label task and can be easily adapted to?
Sorry for the lengthy questions. Really appreciate your time and assistance!
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.