Resuming training from saved state

• python3 training_pipeline.py "model/2020-03-28_22-39-28/epoch0_batch11.par"

Generate scores for a model at different stages throughout its training

• python3 gen_scores.py <model_path> <dataset_file_path.json> [optional eval freq.] [optional eval start step]
• Concrete example (training set): python3 gen_scores.py ./model/MI1_dropout_encodings_only/ preprocessing/data/subset-1/train-subset-1.json 2000 50000 -- this will eval model at step 50000, 52000, 54000, ... up to the most recent one.
• Concrete example (dev set): python3 gen_scores.py ./model/MI1_dropout_encodings_only/ preprocessing/data/dev-v2.0.json
• The dataset file path needs to be something.json and have a corresponding something-tokenized.json for this script to work!
• The script will generate a file scores_<datasetname>.log in the model folder, as well as two plots (EM and F1).
• To copy the plots to your computer run: scp -T guest@138.19.43.95:"'Documents/no_eating_no_drinking/model/MI1_dropout_encodings_only/plot_loss_vs_em_score(train-subset-1).png'" . && scp -T guest@138.19.43.95:"'Documents/no_eating_no_drinking/model/MI1_dropout_encodings_only/plot_loss_vs_f1_score(train-subset-1).png'" . && (or same but with dev-v2 replacing train-subset-1).

Produce answer file for evaluation

• Generate predictions on SQuAD dev set: python3 produce_answers.py model/2020-04-01_01-07-06/epoch0_batch791.par
• Generate predictions on a different dataset: python3 produce_answers.py model/2020-04-01_01-07-06/epoch0_batch791.par preprocessing/data/subset-1/train-subset-1-tokenized.json [optional_prediction_file_path]
• Run evaluation: python3 evaluate-v2.0.py preprocessing/data/subset-1/train-subset-1.json predictions.json

Plot F1 score and loss together

• First generate the scores log file using gen_scores.py (see separate instructions for that).
• Then: python3 plot_f1_vs_loss.py model/mymodel/scores_train-subset-1.log.

Existing impl (atulkum)

• Model: https://github.com/atulkum/co-attention/blob/master/code/model.py
• Batcher: https://github.com/atulkum/co-attention/blob/master/code/data_util/data_batcher.py

Colab training setup

• GDrive: https://drive.google.com/drive/folders/1n5V3Je-qcuncPhkDYikIbAxJ_68d1p4E
• Colab: https://colab.research.google.com/drive/1ycVllF_XIsXDvC4qOAMr4HRTeRaEBFNb

TODOs

• Add your past contributions or nearest-future work here. (everyone)
• Move tests to seperate file (Richie)
• Implement model (everyone)
• Enable cuda usage (Kuba)
• Get forward pass to run (Kuba)
• Get backward pass to run (Kuba -- this was quick)
• Debug why predicted end indices are all 0 (Richie)
• Complete batching (Asmita)
• Create word2id, id2word, embedding matrix (Asmita)
• Training pipeline (Asmita + Kuba + Richie + Dip)
• Model serialisation (Kuba + Richie)
• Debug retain_graph error (Dip)
• Debug training issues (Dip with help from Kuba and Richie)
• Quick script to evaluate latest version of a model automatically (Kuba?)
• Ablation tests:
  • single iteration for s/e indices instead of 4.
  • smaller HIDDEN_DIM
  • try removing some modules or replacing them with something simpler, e.g. coattention with some fully connected layers.
  • Think of more ablation tests. Take ones from the paper.
• Plots:
  • Automate computation of F1/EM scores throughout a model's evolution (training) (Kuba)
  • Plotting F1/EM scores on top of loss (Kuba)
  • Prepare loss tables (discussed in the gdoc)
  • Plotting scores depending on true span length (Dip)
• Generate predictions for evaluation (TODO batching if needed, better conversion from tokens to answer strings, load serialised model) (Dip)

Final stretch doc:

• https://docs.google.com/document/d/1Axe38M8h8__j97_XVvrPySE_Frr2uzN183G8P237Uwk/edit

Our older links

• GDrive: https://drive.google.com/open?id=17K0ZFb_OCdvHgSlkNFErzyx--eYZnoiG
• AML GDoc: https://docs.google.com/document/d/1fit7dYVHn0I0PsAA_HCj3AqnxJ7Wzz-78sb--wxKdA4/edit?usp=sharing
• Group Report: https://www.overleaf.com/8533217623kfnhpsrcnmbf
• Paper: https://arxiv.org/abs/1611.01604