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authorGustaf Rydholm <gustaf.rydholm@gmail.com>2022-02-03 21:40:50 +0100
committerGustaf Rydholm <gustaf.rydholm@gmail.com>2022-02-03 21:40:50 +0100
commit76098a8da9731dd7cba1a7334ad9ae8a2acc760e (patch)
tree9657874b7e93b3ed7d293a92a6f74919d19ca5b0
parenta2e3da61ff3ce3cc1a34d3bec4479ceecb0c274a (diff)
chore: update readme
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make generate
```
+## Train
+
+
+Use, modify, or create a new experiment found at `training/conf/experiment/`.
+To run an experiment we first need to enter the virtual env by running:
+
+```sh
+poetry shell
+```
+
+Then we can train a new model by running:
+
+```sh
+python main.py +experiment=conv_transformer_paragraphs
+```
+
+## Network
+
+Create a picture of the network and place it here
+
+## Graveyard
+
+Ideas of mine that did not work unfortunately:
+
+* Use VQVAE to create pre-train a good latent representation
+ - Tests with various compressions did not show any performance increase compared to training directly e2e, more like decrease to be honest
+ - This is very unfortunate as I really hoped that this idea would work :(
+ - I still really like this idea, and I might not have given up just yet...
+
+
+* Axial Transformer Encoder
+ - Added a lot of extra parameters with no gain in performance
+ - Cool idea, but on a single GPU, nah... not worth it!
-## TODO
## Todo
- [ ] remove einops