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--- |
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license: apache-2.0 |
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language: |
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- pt |
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--- |
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# mRAT-SQL+GAP - Multilingual version of the RAT-SQL+GAP |
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Code and model from our BRACIS 2021 [paper published in Springer Lecture Notes in Computer Science](https://link.springer.com/chapter/10.1007%2F978-3-030-91699-2_35), [here the pre-print in arXiv](https://arxiv.org/abs/2110.03546). |
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Based on: RAT-SQL+GAP: [Github](https://github.com/awslabs/gap-text2sql). Paper: [AAAI 2021 paper](https://arxiv.org/abs/2012.10309) |
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## Abstract |
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mRAT-SQL+GAP is a multilingual version of the RAT-SQL+GAP, wich start with Portuguese Language. Here is available the code, dataset and the results. |
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## Directory Structure |
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Go to the directory where you want to install the structure |
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```bash |
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git clone https://github.com/C4AI/gap-text2sql |
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cd gap-text2sql/mrat-sql-gap |
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``` |
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## Conda mtext2slq Environment Setup |
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```bash |
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conda create --name mtext2sql python=3.7 |
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conda activate mtext2sql |
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conda install pytorch=1.5 cudatoolkit=10.2 -c pytorch |
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pip install gdown |
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conda install -c conda-forge jsonnet |
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pip install -r requirements.txt |
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python -c "import nltk; nltk.download('stopwords'); nltk.download('punkt')" |
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conda install jupyter notebook |
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conda install -c conda-forge jupyter_contrib_nbextensions |
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``` |
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## Setup Script |
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Just run this script below, it will copy the datasets. |
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The original version of the Spider dataset is distributed under the [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode) license. |
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The modified versions (translated to Portuguese, Spanish, French, double-size(English and Portuguese) and quad-size (English, Portuguese, Spanish and French)) of train_spider.json, train_others.json, and dev.json are distributed under the [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode) license, respecting ShareAlike. |
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```bash |
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chmod +x setup.sh |
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./setup.sh |
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``` |
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## Specific setup |
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The models and checkpoints have big files (GBytes), so if you have enough disk space you can run all shell scripts. To understand how things work, run just BART_large.sh and after run the others. |
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```bash |
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./BART_large.sh |
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./mBART50MtoM-large.sh |
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./mT5_large.sh |
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./BERTimbau-base.sh |
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./BERTimbau-large.sh |
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``` |
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## Environment Test |
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Now the environment is ready for training (fine-tune) and inferences. The training is very slow more than 60 hours for BART, BERTimbau, mBART50, and more than 28 hours for mT5. Therefore I recommend testing the environment with the inference. |
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### Preprocess dataset |
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This preprocess step is necessary both for inference and for training. It will take some time, maybe 40 minutes. |
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I will use the script for BART, but you can use the other, look the directory experiments/spider-configs |
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```bash |
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python run.py preprocess experiments/spider-configs/spider-BART-large-en-train_en-eval.jsonnet |
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``` |
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You can see the files processed in the paths: |
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`data/spider-en/nl2code-1115,output_from=true,fs=2,emb=bart,cvlink` |
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## Inference |
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I will use the script for BART again. |
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Note: We are making inferences using the checkpoint already trained (directory logdir) and defined in: |
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`experiments/spider-configs/spider-BART-large-en-train_en-eval.jsonnet` |
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`logdir: "logdir/BART-large-en-train",` and |
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`eval_steps: [40300],` |
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```bash |
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python run.py eval experiments/spider-configs/spider-BART-large-en-train_en-eval.jsonnet |
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``` |
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You then get the inference results and evaluation results in the paths: |
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`ie_dirs/BART-large-en-train/bart-large-en_run_1_true_1-step41000.infer` |
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and |
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`ie_dirs/BART-large-en-train/bart-large-en_run_1_true_1-step41000.eval`. |
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## Training |
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Execute if it is really necessary, if you want to fine-tune the model, this will take a long time. But if you have a good machine available and want to see different checkpoints in the logdir, do it. |
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```bash |
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python run.py train experiments/spider-configs/spider-BART-large-en-train_en-eval.jsonnet |
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``` |
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You then get the training checkpoints in the paths: |
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`logdir/BART-large-en-train` |
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## Checkpoints and Inferences |
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The checkpoints are available here (ESM - Exact Set Matching Accuracy): |
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Paper mRAT-SQL+GAP - Multilingual version of the RAT-SQL+GAP |
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* BART-large trained in English [, ESM all: 0.718] |
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* Checkpoint: [40300](https://drive.google.com/file/d/1F4R-WkJKtJ4lFni3q4lBug6tzSo0H5Qe/view?usp=sharing) |
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* Inference - English: [ESM all: 0.718 - Baseline](https://github.com/C4AI/gap-text2sql/blob/main/mrat-sql-gap/inference-results/BART-large-en-train/spider_eval_match_ratsqlgap-BART-ori-step40300.txt) |
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* BERTimbau-base trained in Portuguese |
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* Checkpoint: [24100](https://drive.google.com/file/d/1gIZS0RuIxdjmm7sNbA3R6p6--9iMJmW8/view?usp=sharing) |
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* Inference - Portuguese: [ESM all: 0.417](https://github.com/C4AI/gap-text2sql/blob/main/mrat-sql-gap/inference-results/BERTimbau-base-pt-train/spider_eval_match_ratsqlgap-bertimbau-base-step24100.txt) |
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* mBART50MtoM-large trained in English |
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* Checkpoint [23100](https://drive.google.com/file/d/16mQf1gMTVGkvONUGpzELzkjCFX5M74cO/view?usp=sharing) |
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* Inference - English: [ESM all: 0.651](https://github.com/C4AI/gap-text2sql/blob/main/mrat-sql-gap/inference-results/mBART50MtoM-large-en-train/spider_eval_match_ratsqlgap-mBART50MtoM-large-en-ori-data-step23100.txt) |
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* mBART50MtoM-large trained in Portuguese |
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* Checkpoint [39100](https://drive.google.com/file/d/1fWPH4bG9-UjW-p6OgmpINWLLsnOopWLh/view?usp=sharing) |
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* Inference - Portuguese: [ESM all: 0.588](https://github.com/C4AI/gap-text2sql/blob/main/mrat-sql-gap/inference-results/mBART50MtoM-large-pt-train/spider_eval_match_ratsqlgap-mBART50MtoM-largeSimplemmaPtEn-step39100.txt) |
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* mBART50MtoM-large trained in English and Portuguese (together) |
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* Checkpoint [41000](https://drive.google.com/file/d/1szb44h_2t3fK2Vc02PdaAjDqnkWqM-0U/view?usp=sharing) |
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* Inference - English: [ESM all: 0.664](https://github.com/C4AI/gap-text2sql/blob/main/mrat-sql-gap/inference-results/mBART50MtoM-large-en-pt-train/41000/spider_eval_match_ratsqlgap-mBART50MtoM-largeSimplemmaPtEn-pt-en-Eval-en-step41000.txt) |
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* Inference - Portuguese: [ESM all: 0.595 Best inferences in Portuguese](https://github.com/C4AI/gap-text2sql/blob/main/mrat-sql-gap/inference-results/mBART50MtoM-large-en-pt-train/41000/spider_eval_match_ratsqlgap-mBART50MtoM-largeSimplemmaPtEn-pt-en-Eval-pt-step41000.txt) |
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* Checkpoint [21100](https://drive.google.com/file/d/1MeLkvGf9-5it1JXnUvU9AmXVnnbAAfP0/view?usp=sharing) |
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* Inference - English: [ESM all: 0.678 Best inferences in English](https://github.com/C4AI/gap-text2sql/blob/main/mrat-sql-gap/inference-results/mBART50MtoM-large-en-pt-train/21100/spider_eval_match_ratsqlgap-mBART50MtoM-largeSimplemmaPtEn-pt-en-Eval-en-step21100.txt) |
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* Inference - Portuguese: [ESM all: 0.581](https://github.com/C4AI/gap-text2sql/blob/main/mrat-sql-gap/inference-results/mBART50MtoM-large-en-pt-train/21100/spider_eval_match_ratsqlgap-mBART50MtoM-largeSimplemmaPtEn-pt-en-Eval-pt-step21100.txt) |
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Future work of the paper mRAT-SQL+GAP |
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* BERTimbau-large trained in Portuguese |
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* Checkpoint: [40100](https://drive.google.com/file/d/1q1NOxisOcIdkMftzGPVxBDn989LDDG3X/view?usp=sharing) |
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* Inference - Portuguese: [ESM all: 0.418](https://github.com/C4AI/gap-text2sql/blob/main/mrat-sql-gap/inference-results/BERTimbau-large-pt-train/spider_eval_match_ratsqlgap-BERTimbau-large-pt-train-Eval-pt-step40100.txt) |
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* mBART50MtoM-large trained in English, Portuguese, Spanish and French (together) - just best inferences |
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* Checkpoint: [39100](https://drive.google.com/file/d/18nioEDEpZf-6CNH_sU3IMZxsSNts_a4y/view?usp=sharing) |
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* Inference - English: [ESM all: 0.696](https://github.com/C4AI/gap-text2sql/blob/main/mrat-sql-gap/inference-results/mBART50MtoM-large-en-pt-es-fr-train/39100/spider_eval_match_ratsqlgap-mBART50MtoM-large-en-pt-es-fr-train_en-eval-step39100.txt) |
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* Checkpoint: [42100](https://drive.google.com/file/d/1AmJjyVHiP9V-FzW9Q1sXge4YMWAP-srg/view?usp=sharing) |
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* Inference - Portuguese: [ESM all pt: 0.626](https://github.com/C4AI/gap-text2sql/blob/main/mrat-sql-gap/inference-results/mBART50MtoM-large-en-pt-es-fr-train/42100/spider_eval_match_ratsqlgap-mBART50MtoM-large-en-pt-es-fr-train_pt-eval-step42100.txt) |
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* Inference - Spanish: [ESM all: 0.628](https://github.com/C4AI/gap-text2sql/blob/main/mrat-sql-gap/inference-results/mBART50MtoM-large-en-pt-es-fr-train/42100/spider_eval_match_ratsqlgap-mBART50MtoM-large-en-pt-es-fr-train_es-eval-step42100.txt) |
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* Checkpoint: [44100](https://drive.google.com/file/d/1P0F218tNkW42Pb7okn3uFyTT5sy4zGZR/view?usp=sharing) |
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* Inference - French: [ESM all: 0.649](https://github.com/C4AI/gap-text2sql/blob/main/mrat-sql-gap/inference-results/mBART50MtoM-large-en-pt-es-fr-train/44100/spider_eval_match_ratsqlgap-mBART50MtoM-large-en-pt-es-fr-train_fr-eval-step44100.txt) |
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Paper mRAT-SQL-FIT |
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* mT5-large trained in English 51Ksteps |
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* Checkpoint: [50100](https://drive.google.com/file/d/1BZ519XxYtXpxxO1iiBy8kSLG4eq34yEX/view?usp=sharing) |
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* Inference - English: [ESM all: 0.684](https://github.com/C4AI/gap-text2sql/blob/main/mrat-sql-gap/inference-results/mT5-large-en-train/spider_eval_match_ratsqlgap-mT5-large-en-train_en-eval-step50100.txt) |
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* mT5-large trained in English, Portuguese, Spanish and French (together) 51Ksteps - just best inferences |
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* Checkpoint: [51100](https://drive.google.com/file/d/1GSQX0dJlsipQPBvYrBqY0SMFhFGCTW0E/view?usp=sharing) |
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* Inference - English: [ESM all: 0.715](https://github.com/C4AI/gap-text2sql/blob/main/mrat-sql-gap/inference-results/mT5-large-en-pt-es-fr-51Ksteps-train/51100/spider_eval_match_ratsqlgap-mT5-large-NoGAP-en-pt-es-fr-train_en-eval-step51100.txt) |
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* Checkpoint: [42100](https://drive.google.com/file/d/1qx2QeZhoygCstZP_QgtLkTlVfBVHzZin/view?usp=sharing) |
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* Inference - Portuguese: [ESM all: 0.680](https://github.com/C4AI/gap-text2sql/blob/main/mrat-sql-gap/inference-results/mT5-large-en-pt-es-fr-51Ksteps-train/42100/spider_eval_match_ratsqlgap-mT5-large-NoGAP-en-pt-es-fr-train_pt-eval-step42100.txt) |
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* Checkpoint: [50100](https://drive.google.com/file/d/1eDm7SHz2il1RYryGLSYVGxt-ozHugJcf/view?usp=sharing) |
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* Inference - Spanish: [ESM all: 0.660](https://github.com/C4AI/gap-text2sql/blob/main/mrat-sql-gap/inference-results/mT5-large-en-pt-es-fr-51Ksteps-train/50100/spider_eval_match_ratsqlgap-mT5-large-NoGAP-en-pt-es-fr-train_es-eval-step50100.txt) |
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* Inference - French: [ESM all: 0.672](https://github.com/C4AI/gap-text2sql/blob/main/mrat-sql-gap/inference-results/mT5-large-en-pt-es-fr-51Ksteps-train/50100/spider_eval_match_ratsqlgap-mT5-large-NoGAP-en-pt-es-fr-train_fr-eval-step50100.txt) |
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* mT5-large trained in English, Portuguese, Spanish and French (together) 120Ksteps - just best inferences |
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* Checkpoint: [77500](https://drive.google.com/file/d/1eUYr_i5O9U1ldm_pBdGozmiRt_42BCh8/view?usp=sharing) |
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* Inference - English: [ESM all: 0.718](https://github.com/C4AI/gap-text2sql/blob/main/mrat-sql-gap/inference-results/mT5-large-en-pt-es-fr-120Ksteps-train//77500/spider_eval_match_ratsqlgap-mT5-large-NoGAP-en-pt-es-fr-train_Div-en-eval-step77500.txt) |
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* Checkpoint: [85500](https://drive.google.com/file/d/1n55OlnyE3RDQtUXMHPwC99Za0xfQavrK/view?usp=sharing) |
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* Inference - Portuguese: [ESM all: 0.675](https://github.com/C4AI/gap-text2sql/blob/main/mrat-sql-gap/inference-results/mT5-large-en-pt-es-fr-120Ksteps-train//85500/spider_eval_match_ratsqlgap-mT5-large-NoGAP-en-pt-es-fr-train_Div-pt-eval-step85500.txt) |
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* Checkpoint: [76500](https://drive.google.com/file/d/1Qs-f2gIgWTJWiWrYGsiULxTBwwpgGatc/view?usp=sharing) |
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* Inference - Spanish: [ESM all: 0.675](https://github.com/C4AI/gap-text2sql/blob/main/mrat-sql-gap/inference-results/mT5-large-en-pt-es-fr-120Ksteps-train//76500/spider_eval_match_ratsqlgap-mT5-large-NoGAP-en-pt-es-fr-train_Div-es-eval-step76500.txt) |
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* Checkpoint: [67500](https://drive.google.com/file/d/1cpTEXMhJXVbJfDc8sW1nfSX91p5VSJtn/view?usp=sharing) |
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* Inference - French: [ESM all: 0.681](https://github.com/C4AI/gap-text2sql/blob/main/mrat-sql-gap/inference-results/mT5-large-en-pt-es-fr-120Ksteps-train//67500/spider_eval_match_ratsqlgap-mT5-large-NoGAP-en-pt-es-fr-train_Div-fr-eval-step67500.txt) |
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* mT5-large trained in English, Portuguese, Spanish and French (together) FIT 120Ksteps - just best inferences |
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* Checkpoint: [105100](https://drive.google.com/file/d/1h0knsFfD6XCXxoEVSFR_I1WdYvMOkcvA/view?usp=sharing) |
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* Inference - English: (simplemma.load_data('en','pt','es','fr')): [ESM all: 0.735 Best inferences in English](https://github.com/C4AI/gap-text2sql/blob/main/mrat-sql-gap/inference-results/mT5-large-FIT-en-pt-es-fr-120Ksteps-train//105100/spider_eval_match_ratsqlgap-mT5-large-NoGAP-120Ksteps-FIT-en-pt-es-fr_Div-en-eval-step105100.txt) |
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* Inference - English: (simplemma.load_data('en'): [ESM all: 0.736 Best inferences in English](https://github.com/C4AI/gap-text2sql/blob/main/mrat-sql-gap/inference-results/mT5-large-FIT-en-pt-es-fr-120Ksteps-train//105100/0.736/spider_eval_match_ratsqlgap-mT5-large-NoGAP-120Ksteps-FIT-en-pt-es-fr_Div-en-eval-step105100.txt) |
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* Checkpoint: [102100](https://drive.google.com/file/d/1VCfLnQgZsrb8lJFkhxzPoyfqc7dEY_K-/view?usp=sharing) |
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* Inference - Portuguese: [ESM all: 0.687](https://github.com/C4AI/gap-text2sql/blob/main/mrat-sql-gap/inference-results/mT5-large-FIT-en-pt-es-fr-120Ksteps-train//102100/spider_eval_match_ratsqlgap-mT5-large-NoGAP-120Ksteps-FIT-en-pt-es-fr_Div-pt-eval-step102100.txt) |
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* Checkpoint: [114100](https://drive.google.com/file/d/13DIB5p97bUnquLpd-dO0-Q1bA9LWjIXD/view?usp=sharing) |
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* Inference - Spanish: [ESM all: 0.689](https://github.com/C4AI/gap-text2sql/blob/main/mrat-sql-gap/inference-results/mT5-large-FIT-en-pt-es-fr-120Ksteps-train//114100/spider_eval_match_ratsqlgap-mT5-large-NoGAP-120Ksteps-FIT-en-pt-es-fr_Div-es-eval-step114100.txt) |
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* Inference - French: [ESM all: 0.698](https://github.com/C4AI/gap-text2sql/blob/main/mrat-sql-gap/inference-results/mT5-large-FIT-en-pt-es-fr-120Ksteps-train//114100/spider_eval_match_ratsqlgap-mT5-large-NoGAP-120Ksteps-FIT-en-pt-es-fr_Div-fr-eval-step114100.txt) |
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* mT5-large trained in English, Portuguese, Spanish and French (together) 2048TKs - 480Ksteps - just inference in English |
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* Checkpoint: [290100](https://drive.google.com/file/d/19Uvfw7QL-8i3yKvybRe9ADLDuSa-MeJv/view?usp=sharing) |
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* Inference - English: [ESM all: 0.697](https://github.com/C4AI/gap-text2sql/blob/main/mrat-sql-gap/inference-results/mT5-large-en-pt-es-fr-2048TKs-480Ksteps-train/290100/spider_eval_match_ratsqlgap-mT5-large-2048TKs-en-pt-es-fr_Div-en-eval-step290100.txt) |
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Other Best Results |
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* T5-v1_1-large trained in English FIT 150Ksteps |
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* Checkpoint: [150300](https://drive.google.com/file/d/14iAERUfhNdU7Gdx9gD9HGuGrnumHaPtq/view?usp=sharing) |
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* Inference - English: [ESM all: 0.736](https://github.com/C4AI/gap-text2sql/blob/main/mrat-sql-gap/inference-results/T5-v1_1-large-FIT-en-150Ksteps-train/spider_eval_match_ratsqlgap-T5-v1_1-large-NoGAP-150Ksteps-FIT-en_One-en-eval-step150300.txt) |
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* mT5-large trained in English, Portuguese, Spanish and French (together) + Non Linear Data Augmentation by rules for extra question 3enr-3ptr-3esr-3frr FIT 150Ksteps |
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* Checkpoint: [128100](https://drive.google.com/file/d/1OjBr9CR0B9feRuk5-Wjh5VpAosB15uNR/view?usp=sharing) |
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* Inference - English: [ESM all: 0.726](https://github.com/C4AI/gap-text2sql/blob/main/mrat-sql-gap/inference-results/mT5-large-FIT-extra-150Ksteps-train/128100/spider_eval_match_ratsqlgap-mT5-large-NoGAP-150Ksteps-FIT-en-pt-es-fr-extra-3enr-3ptr-3esr-3frr_Div-en-eval-step128100.txt) |
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* Checkpoint: [125100](https://drive.google.com/file/d/1bLkTK7qJmwQatK_r6tjSmo6fYYtR4oJ3/view?usp=sharing) |
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* Inference - Portuguese: [ESM all: 0.698](https://github.com/C4AI/gap-text2sql/blob/main/mrat-sql-gap/inference-results/mT5-large-FIT-extra-150Ksteps-train/125100/spider_eval_match_ratsqlgap-mT5-large-NoGAP-150Ksteps-FIT-en-pt-es-fr-extra-3enr-3ptr-3esr-3frr_Div-pt-eval-step125100.txt) |
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* Inference - French: [ESM all: 0.700](https://github.com/C4AI/gap-text2sql/blob/main/mrat-sql-gap/inference-results/mT5-large-FIT-extra-150Ksteps-train/125100/spider_eval_match_ratsqlgap-mT5-large-NoGAP-150Ksteps-FIT-en-pt-es-fr-extra-3enr-3ptr-3esr-3frr_Div-fr-eval-step125100.txt) |
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* Checkpoint: [136100](https://drive.google.com/file/d/1zAcHQS0iNOXRm4iaiqybuAFETfitwBnR/view?usp=sharing) |
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* Inference - Spanish: [ESM all: 0.691](https://github.com/C4AI/gap-text2sql/blob/main/mrat-sql-gap/inference-results/mT5-large-FIT-extra-150Ksteps-train/136100/spider_eval_match_ratsqlgap-mT5-large-NoGAP-150Ksteps-FIT-en-pt-es-fr-extra-3enr-3ptr-3esr-3frr_Div-es-eval-step136100.txt) |
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## Results |
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All intermediate files of the results are in the directory [inference-results](https://github.com/C4AI/gap-text2sql/tree/main/mrat-sql-gap/inference-results). |
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## Security |
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See [CONTRIBUTING](CONTRIBUTING.md#security-issue-notifications) for more information. |
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## License |
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This project is licensed under the Apache-2.0 License. |
