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# Genie NLP library [](https://travis-ci.com/stanford-oval/genienlp) [](https://lgtm.com/projects/g/stanford-oval/genienlp/context:python) This library contains the NLP models for the [Genie](https://github.com/stanford-oval/genie-toolkit) toolkit for virtual assistants. It is derived from the [decaNLP](https://github.com/salesforce/decaNLP) library by Salesforce, but has diverged significantly. The library is suitable for all NLP tasks that can be framed as Contextual Question Answering, that is, with 3 inputs: - text or structured input as _context_ - text input as _question_ - text or structured output as _answer_ As the work by [McCann et al.](https://arxiv.org/abs/1806.08730) shows, many NLP tasks can be framed in this way. Genie primarily uses the library for semantic parsing, paraphrasing, translation, and dialogue state tracking, and this is what the models work best for. ## Installation genienlp is available on PyPi. You can install it with: ```bash pip3 install genienlp ``` After installation, `genienlp` command becomes available. ## Usage ### Training a semantic parser The general form is: ```bash genienlp train --tasks almond --train_iterations 50000 --data <datadir> --save <model_dir> <flags> ``` The `<datadir>` should contain a single folder called "almond" (the name of the task). That folder should contain the files "train.tsv" and "eval.tsv" for train and dev set respectively. To train a BERT-LSTM (or other MLM-based model) use: ```bash genienlp train --tasks almond --train_iterations 50000 --data <datadir> --save <model_dir> \ --model TransformerLSTM --pretrained_model bert-base-cased --trainable_decoder_embedding 50 ``` To train a BART or other Seq2Seq model, use: ```bash genienlp train --tasks almond --train_iterations 50000 --data <datadir> --save <model_dir> \ --model TransformerSeq2Seq --pretrained_model facebook/bart-large --gradient_accumulation_steps 20 ``` The default batch sizes are tuned for training on a single V100 GPU. Use `--train_batch_tokens` and `--val_batch_size` to control the batch sizes. See `genienlp train --help` for the full list of options. **NOTE**: the BERT-LSTM model used by the current version of the library is not comparable with the one used in our published paper (cited below), because the input preprocessing is different. If you wish to compare with published results you should use genienlp <= 0.5.0. ### Inference on a semantic parser In batch mode: ```bash genienlp predict --tasks almond --data <datadir> --path <model_dir> --eval_dir <output> ``` The `<datadir>` should contain a single folder called "almond" (the name of the task). That folder should contain the files "train.tsv" and "eval.tsv" for train and dev set respectively. The result of batch prediction will be saved in `<output>/almond/valid.tsv`, as a TSV file containing ID and prediction. In interactive mode: ```bash genienlp server --path <model_dir> ``` Opens a TCP server that listens to requests, formatted as JSON objects containing `id` (the ID of the request), `task` (the name of the task), `context` and `question`. The server writes out JSON objects containing `id` and `answer`. The server listens to port 8401 by default, use `--port` to specify a different port or `--stdin` to use standard input/output instead of TCP. ### Calibrating a trained model Calibrate the confidence scores of a trained model: 1. Calcualate and save confidence features of the evaluation set in a pickle file: ```bash genienlp predict --task almond --data <datadir> --path <model_dir> --save_confidence_features --confidence_feature_path <confidence_feature_file> ``` 2. Train a boosted tree to map confidence features to a score between 0 and 1: ```bash genienlp calibrate --confidence_path <confidence_feature_file> --save <calibrator_directory> --name_prefix <calibrator_name> ```` 3. Now if you provide `--calibrator_paths` during prediction, it will output confidence scores for each output: ```bash genienlp predict --tasks almond --data <datadir> --path <model_dir> --calibrator_paths <calibrator_directory>/<calibrator_name>.calib ``` ### Paraphrasing Train a paraphrasing model: ```bash genienlp train-paraphrase --train_data_file <train_data_file> --eval_data_file <dev_data_file> --output_dir <model_dir> --model_type gpt2 --do_train --do_eval --evaluate_during_training --logging_steps 1000 --save_steps 1000 --max_steps 40000 --save_total_limit 2 --gradient_accumulation_steps 16 --per_gpu_eval_batch_size 4 --per_gpu_train_batch_size 4 --num_train_epochs 1 --model_name_or_path <gpt2/gpt2-medium/gpt2-large/gpt2-xlarge> ``` Generate paraphrases: ```bash genienlp run-paraphrase --model_name_or_path <model_dir> --temperature 0.3 --repetition_penalty 1.0 --num_samples 4 --batch_size 32 --input_file <input_tsv_file> --input_column 1 ``` ### Translation Use the following command for training/ finetuning an NMT model: ```bash genienlp train --train_tasks almond_translate --data <data_directory> --train_languages <src_lang> --eval_languages <tgt_lang> --no_commit --train_iterations <iterations> --preserve_case --save <save_dir> --exist_ok --skip_cache --model TransformerSeq2Seq --pretrained_model <hf_model_name> ``` We currently support MarianMT, MBART, MT5, and M2M100 models.<br> To save a pretrained model in genienlp format without any finetuning, set train_iterations to 0. You can then use this model to do inference. To produce translations for an eval/ test set run the following command: ```bash genienlp predict --tasks almond_translate --data <data_directory> --pred_languages <src_lang> --pred_tgt_languages <tgt_lang> --path <path_to_saved_model> --eval_dir <eval_dir> --skip_cache --val_batch_size 4000 --evaluate <valid/test> --overwrite --silent ``` If your dataset is a document or contains long examples, pass `--translate_example_split` to break the examples down into individual sentences before translation for better results. <br> To use alignment as described in our localization paper (cited below), use `--replace_qp` and `--force_replace_qp` which ensures the parameters between quotations marks in the sentence are preserved in the output. The alignment code has been updated and improved since 0.6.0 release, so if you wish to compare the results use genienlp <=0.6.0. However, we recommend using the newer version for higher translation quality. ### Named Entity Disambiguation First run a bootleg model to extract mentions, entity candidates, and contextual embeddings for the mentions. ```bash genienlp bootleg-dump-features --train_tasks <train_task_names> --save <savedir> --preserve_case --data <dataset_dir> --train_batch_tokens 1200 --val_batch_size 2000 --database_type json --database_dir <database_dir> --min_entity_len 1 --max_entity_len 4 --bootleg_model <bootleg_model> ``` This command generates several output files. In `<dataset_dir>` you should see a `prep` dir which contains preprocessed data (e.g. data converted to memory-mapped format, several array to facilitate embedding lookup etc.) If your dataset doesn't change you can reuse the same files. It will also generate several files in <results_temp> folder. In `eval_bootleg/[train|eval]/<bootleg_model>/bootleg_lables.jsonl` you can see the examples, mentions, predicted candidates and their probabilities according to bootleg. Now you can use the extracted features from bootleg in downstream tasks such as semantic parsing to improve named entity understanding and consequently generation: ```bash genienlp train --train_tasks <train_task_names> --train_iterations <iterations> --preserve_case --save <savedir> --data <dataset_dir> --model TransformerSeq2Seq --pretrained_model facebook/bart-base --train_batch_tokens 1000 --val_batch_size 1000