Updated todos in readme

1 files changed, 8 insertions, 26 deletions

diff --git a/README.md b/README.md
index dac7e98..ed93955 100644
--- a/README.md
+++ b/README.md
@@ -11,44 +11,26 @@ TBC
 
 Extract text from the iam dataset:
 ```
-poetry run extract-iam-text --use_words --save_text train.txt --save_tokens letters.txt
+poetry run python extract-iam-text --use_words --save_text train.txt --save_tokens letters.txt
 ```
 
 Create word pieces from the extracted training text:
 ```
-poetry run make-wordpieces --output_prefix iamdb_1kwp --text_file train.txt --num_pieces 100
+poetry run python make-wordpieces --output_prefix iamdb_1kwp --text_file train.txt --num_pieces 100
 ```
 
 Optionally, build a transition graph for word pieces:
 ```
-poetry run build-transitions --tokens iamdb_1kwp_tokens_1000.txt --lexicon iamdb_1kwp_lex_1000.txt --blank optional --self_loops --save_path 1kwp_prune_0_10_optblank.bin --prune 0 10
+poetry run python build-transitions --tokens iamdb_1kwp_tokens_1000.txt --lexicon iamdb_1kwp_lex_1000.txt --blank optional --self_loops --save_path 1kwp_prune_0_10_optblank.bin --prune 0 10
 ```
 (TODO: Not working atm, needed for GTN loss function)
 
 ## Todo
-- [x] create wordpieces
-  - [x] make_wordpieces.py
-  - [x] build_transitions.py
-  - [x] transform that encodes iam targets to wordpieces
-  - [x] transducer loss function
-- [ ] Train with word pieces
-  - [ ] Pad word pieces index to same length
-- [ ] Local attention in first layer of transformer
-- [ ] Halonet encoder
-- [ ] Implement CPC
-  - [ ] https://arxiv.org/pdf/1905.09272.pdf
-  - [ ] https://pytorch-lightning-bolts.readthedocs.io/en/latest/self_supervised_models.html?highlight=byol
-
-
-- [ ] Predictive coding
-  - https://arxiv.org/pdf/1807.03748.pdf
-  - https://arxiv.org/pdf/1904.05862.pdf
-  - https://arxiv.org/pdf/1910.05453.pdf
-  - https://blog.evjang.com/2016/11/tutorial-categorical-variational.html
-
-
-
-
+- [ ] Reimplement transformer from scratch
+- [ ] Implement Nyström attention (for efficient attention)
+- [ ] Dino 
+- [ ] Efficient-net b0 + transformer decoder
+- [ ] Test encoder pre-training ViT (CvT?) with Dino, then train decoder in a separate step
 
 
 ## Run Sweeps