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Multi-task learning#7

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okyksl wants to merge 61 commits intomainthe-deep/deep-experiments:mainfrom 1D-matrixthe-deep/deep-experiments:1D-matrixCopy head branch name to clipboard

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Commits

Commits on Dec 3, 2021

• feat(mlflow): artifact logging

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  okyksl

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  4e62012

• feat(data): add maximum tokenizer length as an option

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  okyksl

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  9bf8cd6

• feat(data): add inference option

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• feat(modeling): use a dynamically provided threshold for groups

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  1a54e3e

• feat(mlflow): add mlflow prediction wrapper

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  okyksl

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  b251fa8

• feat(mlflow): add artifact for inference params

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  47adf42

• fix(mlflow): use sigmoid on raw network scores

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• fix(mlflow): rename mlflow.py to infer.py

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  okyksl

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• fix(data): prepare targets and groups

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• fix(data): provide targets in non-inference mode

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• docs(mlflow): add additional considerations to MLFlow deployment

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• fix(data): add token types only if it is supported

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• feat(mlflow): add deployment option

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• chore(): delete TODO file

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• refactor(mlflow): use 'predictions' instead of 'logits'

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• fix(mlflow): use artifact uri in logging model

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  okyksl

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  71a2c63

• fix(mlflow): use proper artifact and code paths

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• refactor(mlflow): log label artifacts with the model

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• feat(data): preprocessing for offline testing format

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• feat(sagemaker): add inference job using MLFlow 'log_model'

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• feat(runner): reload best model in training

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• feat(mlflow): add tokenizer options as arguments to mlflow

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• fix(mlflow): access artifacts in the pyfunc context object

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  okyksl

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• refactor(data): update filtering info message

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• refactor(data): make 'tokenizer_max_len' optional

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  okyksl

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  4f6a050

• fix(data): use source length to return dataset length

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• fix(data): drop empty rows in preprocessing

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• fix(mlflow): cast predictions and probabilities to string

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• fix(sagemaker): use fixed 'source' field by renaming prior to job execution

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• chore(params): fix 'epoch' hyperparameter

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• fix(data): apply literal_eval for filtering

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• feat(modeling): add focal loss-star

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• fix(mlflow): upload full code path

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• fix(mlflow): add deployment dependencies

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• feat(data): compute target statistics

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• feat(data): support pickle, csv and excel dataframes

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• feat(modeling): add inverse loss weighting

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• feat(data): support sectors as a 1-head classification task

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• docs(modeling): add documentation to multi task transformer

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• fix(modeling): return zero if coarse group is predicted as negative

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  44c9634

• feat(modeling): add inverse loss weighting

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  okyksl

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  eb36bdc

• feat(mlflow): update model output format

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• refactor(data): modularize dataset into text and target datasets

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• feat(data): support multi-task learning

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• refactor(modeling): refactor and document modeling

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• fix(eval): test on all samples

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• fix(modeling): do not explicitly pass num_heads to models

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• feat(sagemaker): use new data version

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Commits on Dec 5, 2021

• feat(data): use new virtual analysis framework

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• feat(dataset): have an option to exclude unwanted target labels

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• fix(modeling): initialize module list correctly

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• feat(runner): exclude "NOT_MAPPED" targets

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• fix(runner): handle multi-task targets correctly

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• refactor(data): better error messages when groups and group_names are not in same length

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  8eb627b

Commits on Dec 15, 2021

• fix(data): compute stats for multi-head dataset

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  okyksl

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  0b6b1d7

• refactor(modeling): rename MultiTargetHead to MultiTargetTransformer

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• feat(data): add iterative option for labels

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• feat(eval): add multihead metrics

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• fix(modeling): work in iterative settings

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• feat(trainer): multi-task trainer

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Commits on Dec 16, 2021

• fix(runner): fix evaluation and training logic for multi-task learning

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