LITMUS Predictor provides support for simulating performance in ~100 languages given training observations of the desired task-model. Each training observation specifies the finetuning-datasize + test-performance in different languages.
Further, the tool provides support for constructing a data-collection strategy to maximize performance in desired targets subject to different constraints.
pip install -U pip
pip install -r requirements.txt
litmus/litmus_mixing.py contains the implementation of the LITMUS Predictor which can be trained on observations of different task-model trainings.
usage: LITMUS Tool [-h] [--scores_file SCORES_FILE]
[--train_format {json,csv}] [--save_state SAVE_STATE]
[--load_state LOAD_STATE]
[--precomputed_features PRECOMPUTED_FEATURES]
[--pivot_features {none,all,data_only}] [--use_all_langs]
[--common_scaling] [--training_algorithm {xgboost,mlp}]
[--error_method {LOO,LOTO,split,kfold,manual_split}]
[--data_sizes DATA_SIZES] [--mode MODE [MODE ...]]
[--output_dir OUTPUT_DIR]
[--heatmap_targets HEATMAP_TARGETS]
[--suggestions_budget SUGGESTIONS_BUDGET]
[--suggestions_langbudget SUGGESTIONS_LANGBUDGET]
[--suggestions_targets SUGGESTIONS_TARGETS]
[--suggestions_weights SUGGESTIONS_WEIGHTS]
[--suggestions_pivots SUGGESTIONS_PIVOTS]
[--suggestions_augmentable SUGGESTIONS_AUGMENTABLE]
[--suggestions_grid {exponential,linear}]
[--suggestions_objective {avg,min}]
[--suggestions_minperf SUGGESTIONS_MINPERF]
[--suggestions_minlangperf SUGGESTIONS_MINLANGPERF]
[--suggestions_verbose]
{mbert,xlmr}
positional arguments:
{mbert,xlmr} name of model to use
optional arguments:
-h, --help show this help message and exit
--scores_file SCORES_FILE
path of json file containing scores to train on
--train_format {json,csv}
Format of the training data
--save_state SAVE_STATE
Save state of training of model to pickle file
--load_state LOAD_STATE
Load trained model from pickle file
--precomputed_features PRECOMPUTED_FEATURES
Path to precomputed-features file.
--pivot_features {none,all,data_only}
What features based on pivot langs to use
--use_all_langs Add features based on all langs the tool supports
(Needed for transfer)
--common_scaling Common min max scaling params that are pvt
dependent(data size, type overlap, distance)
--training_algorithm {xgboost,mlp}
which regressor to use
--error_method {LOO,LOTO,split,kfold,manual_split}
--data_sizes DATA_SIZES
Pivot data-size configs (semi-colon separated configs,
each config itself being comma-separated key-value
pairs)
--mode MODE [MODE ...]
Output modes (comma-separated). Choose from following:
{heatmap, suggestions}.
--output_dir OUTPUT_DIR
Overrride output directory
--heatmap_targets HEATMAP_TARGETS
Targets for heatmap. Overrides suggestions_targets
(which is used by deafult)
--suggestions_budget SUGGESTIONS_BUDGET
Budget for finding suggestions of which languages to
add data for (0 to disable)
--suggestions_langbudget SUGGESTIONS_LANGBUDGET
Language-specific budget for finding suggestions
(overrrides suggestions_budget for these langs, comma-
separated list of key:value pairs)
--suggestions_targets SUGGESTIONS_TARGETS
Targets being considered (comma-separated)
--suggestions_weights SUGGESTIONS_WEIGHTS
Target weights for avg perf objective (comma-separated
list of key:value pairs, default wt=1)
--suggestions_pivots SUGGESTIONS_PIVOTS
Index of desired row in data_sizes
--suggestions_augmentable SUGGESTIONS_AUGMENTABLE
Set of augmentable languages (comma-separated)
--suggestions_grid {exponential,linear}
Search space grid to use for suggestions
--suggestions_objective {avg,min}
Objective function to be used for finding suggestions
--suggestions_minperf SUGGESTIONS_MINPERF
Minimum acceptable average performance across tgts
--suggestions_minlangperf SUGGESTIONS_MINLANGPERF
Minimum acceptable performance for given tgts (comma-
separated list of key:value pairs)
--suggestions_verbose
Verbose logging of search
python3 litmus_mixing.py xlmr --scores_file training_observations.json --common_scaling --error_method split --mode heatmap --data_sizes "en:1000,hi:1000;en:1000,ar:1000" --use_all_langs --heatmap_targets en,fr,de,hi,ar,ru
from litmus import litmus_mixing
data_file = "" # Location of train data file
args = litmus_mixing.parse_args([
"xlmr", data_file,
"--common_scaling",
"--error_method", "kfold",
"--training_algorithm", "xgboost"
])
res = litmus_mixing.litmus_main(args)frontend/ contains the code for hosting the tool as a webapp using Azure Functions. frontend/WebUx implements the client-side as a static website which interacts with a Azure Functions backend which internally runs the litmus/litmus_mixing.py script.
- Create an Azure Functions resource on Azure.
- Install Azure CLI and Functions Core Tools
cdinto thefrontend/directory and deploy to azure functions usingfunc azure functionapp publish <function-resource-name>.
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