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SuperLim

This repository implements experiments/baselines for SuperLim 2.

Reproduce results

Setup Environment (optional)

1. wget https://repo.anaconda.com/archive/Anaconda3-2022.10-Linux-x86_64.sh
2. bash Anaconda3-2022.10-Linux-x86_64.sh
3. yes, yes, yes
4. conda create -n ptgpu_venv python=3.9
5. conda activate ptgpu_venv
6. conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia

Setup Repository (optional)

1. git clone [email protected]:aidotse/superlim-baselines.git
2. cd superlim-baselines
3. pip install -r requirements.txt
4. tmux new -s exp
5. tmux attach -t exp

Run Experiments

1. Download all data to data directory 
2. Setup wandb key in api_wandb_key.txt
3. Configure GPUs to use in run_bert_experiments.sh
4. Specify models and tasks in run_bert_experiments.sh
5. Specify accumulation sizes for models in bert/bert_experiment_driver.py to suit your available GPU memory.
6. ./run_dummy_experiments.sh
7. ./run_bert_experiments.sh (this includes gpt experiments if wanted)
8. Run the collect_results/create_table.py script, it will collect results in results/experiments/metrics directory and create the results/experiments/metrics/model_deliverables directory with json files.

This will fine-tune 672 models in total, if the 4 gpt-models are included (they are not by default). 84 models will be saved, one for each task-model combination.

Find The Results

1. Individual experiment results: results/experiments/metrics/
2. Packed results grouped by model: results/experiments/metrics/model_deliverables/
3. Model predictions on dev and test files: results/experiments/predictions/
4. Best fine-tuned models: results/experiments/models/

The tasks are named slightly different in this repository since it was developed during the development of SuperLim 2. See task_to_info_dict in Experiment.py for the task names used. When the final results files are created, these names are mapped to the official names.

Note: Winogender will be evaluated automatically when training/evaluating with SweMNLI.


Repository development guide

The class hierarchy handles much of the execution flow, like a framework.

Utils

  • paths.py defines paths that can be imported, and should work cross-platform and in docker containers etc
  • compute_metrics.py implements functions to compute metrics required for the experiments
  • collect_metrics.py loads the experiment results in the pre-defined results file hierarchy and plots a table.

Class Hierarchy

Experiment.py

Experiment.py defines the abstract class Experiment, which handles the experiment execution flow, keeps track of the model name, task name, and other meta-data. It also handles the writing of results to file, and contains task_to_info_dict which should contain the required meta-data for each task, such as which metric is used.

ExperimentBert Class

ExperimentBert.py defines another abstract class ExperimentBert, which handles loading of tokenizers and models, default hyperparameters, hyperparameter tuning, HuggingFace Trainer, Evaluation, etc.

ExperimentBert Children

Children of ExperimentBert simply define the task name, which should be supported in Experiment.py, and implements the abstract method create_dataset, which calls the _load_data method and preprocesses it to make it ready for training.

ExperimentDummy

Inherits Experiment, and uses sklearn.dummy to provide a dummy baseline. This works for any task that has training data and is a classification or regression problem.

Adding a new task

  1. Make sure the data is accessible, preferably via HF Datasets, or otherwise through JSONL/TSV files
  2. Create a function reformat_dataset_<task_name> in dataset_loader.py, that converts the dataset into a dataset of desired format.
  3. Add a pointer to this function in the dictionary TASK_TO_REFORMAT_FUN above the load_dataset_by_task function in the same file.
  4. Add an entry with the required meta-data for this task in Experiment.py, in the dictionary task_to_info_dict.
  5. Add an entry in bert_experiment_driver.py for the corresponding ExperimentBert class
  6. The dataset, reformatting, and meta-data loading is done automatically when a child instance of Experiment is used.

Results structure

  • Metric results are stored in json-files with the following path: results/experiments/metrics/<task_name>/<model_name>/metrics.json

  • Ray (library for HPS) results are stored in results/ray_results, and can be cleared after the experiments are done. Can be done automatically with clear_result_checkpoints.sh.

  • HF Trainer checkpoints are stored in results/trainer_output, and can be cleared after the experiments are done. This is done automatically in the BertExperiment class, and with clear_result_checkpoints.sh

Results

All tasks are evaluated using the Krippendorff Alpha metric (nominal for classification, and interval for regression).

Evaluation Results

Model ABSA Argumentation DaLAJ SweMNLI SweParaphrase SweWiC SweWinograd Swedish FAQ Avg ↑
KBLab/megatron-bert-large-swedish-cased-165k 0.571564 0.946352 0.727593 0.776482 0.908897 0.312644 0.258708 0.883748 0.673248
AI-Nordics/bert-large-swedish-cased 0.533768 0.936037 0.716007 0.746819 0.895458 0.289614 0.240471 0.83509 0.649158
KB/bert-base-swedish-cased 0.513309 0.929816 0.715983 0.720911 0.900527 0.408507 0.188203 0.747808 0.640633
KBLab/megatron-bert-base-swedish-cased-600k 0.497364 0.936032 0.690426 0.744255 0.895231 0.299891 0.179137 0.835171 0.634688
xlm-roberta-large 0.554599 0.933846 0.71126 0.783054 0.908649 0.345008 0.151711 0.679484 0.633452
NbAiLab/nb-bert-base 0.440776 0.923843 0.638692 0.717407 0.87952 0.351808 0.177085 0.718147 0.60591
KBLab/bert-base-swedish-cased-new 0.476216 0.915303 0.726241 0.727923 0.812494 0.225232 0.0651066 0.553061 0.562697
xlm-roberta-base 0.398698 0.917526 0.671511 0.720407 0.871351 0.253994 -0.251163 0.619826 0.525269
SVM 0.336347 0.906789 0.501149 0.126261 0.175726 0.012941 0.0981801 0.0473189 0.275589
Decision Tree 0.206274 0.862748 0.298952 0.14797 0.223647 0.0405762 -0.0220252 -0.02735 0.216349
Random -0.0566656 0.00489102 -0.0357937 0.00267875 -0.033005 0.0568082 0.0924728 -0.118317 -0.0108663
Random Forest 0.0120151 -0.256239 -0.31043 -0.255086 0.159126 0.0272561 -0.251163 0.00746468 -0.108382
MaxFreq/Avg -0.0309908 -0.256239 -0.347135 -0.343683 -0.0246646 -0.332 -0.251163 -0.316185 -0.237757

Test Results

Model ABSA Argumentation DaLAJ SweMNLI SweParaphrase SweWiC SweWinograd Swedish FAQ Avg ↑
KBLab/megatron-bert-large-swedish-cased-165k 0.508299 0.627597 0.753261 0.231612 0.873878 0.307947 0.188953 0.777436 0.533623
AI-Nordics/bert-large-swedish-cased 0.480036 0.563173 0.745449 0.240594 0.862311 0.316317 0.191522 0.718673 0.51476
KB/bert-base-swedish-cased 0.529183 0.555028 0.739715 0.179116 0.844865 0.37619 0.139458 0.640648 0.500525
xlm-roberta-large 0.51631 0.583698 0.737508 0.20472 0.881687 0.3672 0.0806007 0.583791 0.494439
KBLab/megatron-bert-base-swedish-cased-600k 0.449322 0.562494 0.718029 0.217683 0.866812 0.277146 0.0614488 0.709154 0.482761
NbAiLab/nb-bert-base 0.389723 0.540602 0.64446 0.171583 0.822616 0.325909 0.120361 0.659844 0.459387
KBLab/bert-base-swedish-cased-new 0.427938 0.553602 0.753263 0.16292 0.754713 0.140347 0.0420433 0.446627 0.410182
xlm-roberta-base 0.365947 0.497157 0.700577 0.185628 0.812797 0.181145 -0.177215 0.473112 0.379893
SVM 0.285916 0.353759 0.517739 0.000204149 0.23909 0.0422635 0.0549607 0.0381895 0.191515
Decision Tree 0.117238 0.155629 0.268636 0.0132697 0.199644 0.0398626 -0.24 0.0399946 0.0742843
Random 0.00783217 0.0132383 0.00702486 -0.0906326 -0.0427819 -0.00954447 0.0806007 -0.150356 -0.0230774
Random Forest 0.00537142 -0.272389 -0.312481 -0.411051 0.142812 0.00334587 -0.177215 0.0318551 -0.123719
MaxFreq/Avg -0.0517904 -0.272389 -0.340028 -0.433837 -0.00149459 -0.332667 -0.177215 -0.309699 -0.23989

SweWinogender Results

Model SweWinogender (Parity) SweWinogender (Alpha)
KBLab/megatron-bert-large-swedish-cased-165k 0.995192 -0.29472
xlm-roberta-base 0.995192 -0.298024
xlm-roberta-large 0.985577 -0.315893
KBLab/megatron-bert-base-swedish-cased-600k 0.990385 -0.320952
KBLab/bert-base-swedish-cased-new 1 -0.328369
AI-Nordics/bert-large-swedish-cased 1 -0.332265
KB/bert-base-swedish-cased 1 -0.332265
NbAiLab/nb-bert-base 0.990385 -0.332306

Best Hyperparameters

The search space for all tasks and transformer models were the following:

{
    "learning_rate": [1e-5, 2e-5, 3e-5, 4e-5],
    "batch_size": [16, 32]
}

with the exception of SweMNLI, that had its search space reduced to the immense training set size:

{
    "learning_rate": [1e-5, 4e-5],
    "batch_size": [16, 32]
}

Furthermore, all models use the following hyperparameters along with HuggingFace Trainer's default arguments:

{
    "warmup_ratio": 0.06,
    "weight_decay": 0.1,  # 0.0 if gpt
    "num_train_epochs": 10,
    "fp16": true
}

num_train_epochs=10 is the maximum epochs, using early stopping with patience=5.

Model Hyperparameters Tables

Below follow the selected Hyperparameters for each model and task, along with a standard deviation of the evaluation metric for the different configurations.

AI-Nordics/bert-large-swedish-cased

Task LR BS hps std
ABSA 2e-05 16 0.0120759
Argumentation 1e-05 32 0.00375441
DaLAJ 4e-05 32 0.0138314
SweMNLI 1e-05 16 0.00832644
SweParaphrase 3e-05 16 0.00558327
SweWiC 3e-05 32 0.0153254
SweWinograd 3e-05 32 0.0308409
Swedish FAQ 1e-05 32 0.0166277

KB/bert-base-swedish-cased

Task LR BS hps std
ABSA 2e-05 16 0.02115
Argumentation 3e-05 32 0.00774597
DaLAJ 2e-05 32 0.00690644
SweMNLI 1e-05 32 0.0118903
SweParaphrase 4e-05 32 0.00267101
SweWiC 2e-05 16 0.0111782
SweWinograd 1e-05 16 0.0618928
Swedish FAQ 1e-05 16 0.0258529

KBLab/bert-base-swedish-cased-new

Task LR BS hps std
ABSA 2e-05 16 0.010503
Argumentation 4e-05 32 0.463319
DaLAJ 2e-05 16 0.00939234
SweMNLI 1e-05 16 0.00648224
SweParaphrase 4e-05 16 0.0423114
SweWiC 1e-05 32 0.171214
SweWinograd 1e-05 32 0.132972
Swedish FAQ 3e-05 32 0.144674

KBLab/megatron-bert-base-swedish-cased-600k

Task LR BS hps std
ABSA 4e-05 16 0.0215247
Argumentation 3e-05 16 0.0777753
DaLAJ 4e-05 16 0.0171051
SweMNLI 1e-05 16 0.00194938
SweParaphrase 4e-05 16 0.00612823
SweWiC 4e-05 16 0.0291987
SweWinograd 4e-05 32 0.114922
Swedish FAQ 3e-05 32 0.00878437

KBLab/megatron-bert-large-swedish-cased-165k

Task LR BS hps std
ABSA 3e-05 16 0.0126327
Argumentation 4e-05 16 0.0226433
DaLAJ 3e-05 32 0.0174812
SweMNLI 1e-05 32 0.00384093
SweParaphrase 4e-05 16 0.00475201
SweWiC 4e-05 32 0.0130878
SweWinograd 3e-05 16 0.0664638
Swedish FAQ 4e-05 16 0.00752451

NbAiLab/nb-bert-base

Task LR BS hps std
ABSA 4e-05 16 0.0263801
Argumentation 3e-05 16 0.0194445
DaLAJ 1e-05 16 0.00804185
SweMNLI 1e-05 16 0.0108116
SweParaphrase 4e-05 32 0.00655906
SweWiC 4e-05 32 0.0228019
SweWinograd 4e-05 32 0.029244
Swedish FAQ 3e-05 16 0.330018

xlm-roberta-base

Task LR BS hps std
ABSA 4e-05 16 0.0325399
Argumentation 2e-05 16 0.029516
DaLAJ 2e-05 32 0.0173028
SweMNLI 1e-05 16 0.0144903
SweParaphrase 1e-05 16 0.00433707
SweWiC 1e-05 16 0.233132
SweWinograd 2e-05 16 0
Swedish FAQ 1e-05 16 0.352092

xlm-roberta-large

Task LR BS hps std
ABSA 2e-05 16 0.240555
Argumentation 3e-05 32 0.512098
DaLAJ 1e-05 32 0.477851
SweMNLI 1e-05 32 0.471841
SweParaphrase 1e-05 16 0.00389993
SweWiC 1e-05 32 0.31005
SweWinograd 2e-05 32 0.128864
Swedish FAQ 2e-05 32 0.454154

Average Standard Deviation of HPS

The following table shows the average standard deviation of the hyperparameter configuration performances. Sorted on avg std and could indicate hyperparameter sensitivity of the models.

Model avg std
AI-Nordics/bert-large-swedish-cased 0.0132957
KBLab/megatron-bert-large-swedish-cased-165k 0.0185533
KB/bert-base-swedish-cased 0.018661
KBLab/megatron-bert-base-swedish-cased-600k 0.0346735
NbAiLab/nb-bert-base 0.0566626
xlm-roberta-base 0.0854263
KBLab/bert-base-swedish-cased-new 0.122608
xlm-roberta-large 0.324914

Average Mean Distance to Max Metric

The following table shows the average of the mean distances to the maximum achieved performance. I.e. for each task hyperparameter search, take the mean of the metric distances to the maximum hyperparameter configuration.

Model avg mean distance
KBLab/megatron-bert-large-swedish-cased-165k 0.0242377
AI-Nordics/bert-large-swedish-cased 0.0243757
KB/bert-base-swedish-cased 0.0294655
KBLab/megatron-bert-base-swedish-cased-600k 0.0472022
NbAiLab/nb-bert-base 0.0486684
xlm-roberta-base 0.0871857
KBLab/bert-base-swedish-cased-new 0.122801
xlm-roberta-large 0.35873

Notes / Deviations

SweFAQ Non-transformer baselines

The random baseline takes random values from the range of all seen labels, not the current number of possible answers.

Traditional ML baselines take a random answer from the candidates that the models independently predict as a correct answer.

SweMNLI Traditional ML baselines

For these traditional ML baselines, only 5% (20,000 samples) of the training set is used for training. This did not seem to have a noticeable effect on the end performance, and the motivation for this was to reduce the training time.