Comments (12)
Can you please tell the exact commands you used?
from embedkgqa.
parser = argparse.ArgumentParser()
parser.add_argument('--dataname', type=str, default='metaQA')
parser.add_argument('--hops', type=str, default='3')
parser.add_argument('--ls', type=float, default=0.0)
parser.add_argument('--validate_every', type=int, default=5)
parser.add_argument('--model', type=str, default='ComplEx')
parser.add_argument('--kg_type', type=str, default='full')
parser.add_argument('--mode', type=str, default='train')
parser.add_argument('--batch_size', type=int, default=128)
parser.add_argument('--dropout', type=float, default=0.1)
parser.add_argument('--entdrop', type=float, default=0.1)
parser.add_argument('--reldrop', type=float, default=0.2)
parser.add_argument('--scoredrop', type=float, default=0.2)
parser.add_argument('--l3_reg', type=float, default=0.0)
parser.add_argument('--decay', type=float, default=1.0)
parser.add_argument('--shuffle_data', type=bool, default=True)
parser.add_argument('--num_workers', type=int, default=15)
parser.add_argument('--lr', type=float, default=0.0005)
parser.add_argument('--nb_epochs', type=int, default=90)
parser.add_argument('--gpu', type=int, default=4)
parser.add_argument('--neg_batch_size', type=int, default=128)
parser.add_argument('--hidden_dim', type=int, default=256)
parser.add_argument('--embedding_dim', type=int, default=256)
parser.add_argument('--relation_dim', type=int, default=200)
parser.add_argument('--use_cuda', type=bool, default=True)
parser.add_argument('--patience', type=int, default=5)
parser.add_argument('--freeze', type=str2bool, default=True)
from embedkgqa.
Are you using the pretrained KG embeddings or did you retrain them?
from embedkgqa.
Yes, I retrained it by your command in #11.
from embedkgqa.
Those aren't the best hyperparameters in #11, I just wrote that command to clarify that it does work even when batch_norm is set to 0. Please set batch_norm=1, and if possible, please let me know the MRR you get when training full MetaQA KG embedding. It should be very close to 1.0, if not 1.0 .
from embedkgqa.
Also, as mentioned in #15, we need relation scoring module for 3-hop SOTA performance. Without it we get ~0.75 test accuracy
from embedkgqa.
if set batch_norm=1, #11 is the best hyperparameters ?
from embedkgqa.
Let me confirm, please wait 15-20 minutes
from embedkgqa.
@ToneLi Yes, please try same command with batch_norm = 1. As explained in #11, this is needed because of the implementation of KG embedding done by https://github.com/ibalazevic/TuckER from which this code has been taken
from embedkgqa.
Closing this for now, please reopen if issue remains @ToneLi
from embedkgqa.
Hi, Sorry to disturb you, I used the hyperparamaters, why I get the HIT value is 1. It's the right result? (FULL KG metaQA)
from embedkgqa.
I train KG embedding again, but I still cannot get 0.49 in metaQA-full KG-3hops, do you have any advice to get 0.75 you mentioned?
from embedkgqa.
Related Issues (20)
- relation in fbwq_full
- relation matching
- MetaQA relation match HOT 1
- About ComplEx score calculation HOT 1
- The MetaQA dataset HOT 2
- Question with "half KG" protocol HOT 2
- How to build your own pruning_ train.txt
- Hello I have some questiones about the code such as what is the meaning of "best_valid" HOT 1
- Do you use the folder "train_embeddings"?
- I have some question in the relation matching
- When I run RoBERTa / main.py, running to ' creating model ' GPU takes up 0 and takes several hours to create the model.
- Is it possible to share the pdf version or ppt version slides
- How to use eval? How can I use pre trained model for QA? HOT 1
- RoBERTa used for question embedding
- Pretrained models missing HOT 1
- How to set up fbwq_full?
- miss files HOT 2
- About dataset
- pretrained_models.zip HOT 7
- No found pretrained_model
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from embedkgqa.