├── setup.cfg ├── logo.png ├── textgenie ├── __init__.py ├── textgenie.py └── grammar_utils.py ├── examples ├── basic.py └── examples.ipynb ├── setup.py ├── sentences_aug.txt ├── .gitignore ├── README.md └── LICENSE /setup.cfg: -------------------------------------------------------------------------------- 1 | [metadata] 2 | description-file = README.md -------------------------------------------------------------------------------- /logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hetpandya/textgenie/HEAD/logo.png -------------------------------------------------------------------------------- /textgenie/__init__.py: -------------------------------------------------------------------------------- 1 | from .textgenie import TextGenie 2 | 3 | __version__ = "0.1.9.7b" 4 | __author__ = "Het Pandya" 5 | __license__ = "MIT" 6 | -------------------------------------------------------------------------------- /examples/basic.py: -------------------------------------------------------------------------------- 1 | # coding: utf-8 2 | 3 | from textgenie import TextGenie 4 | 5 | t5_model = "hetpandya/t5-base-tapaco" 6 | bert_model = "microsoft/deberta-v3-large" 7 | 8 | textgenie = TextGenie(t5_model, bert_model, spacy_model_name="en_core_web_lg", device="cuda") 9 | 10 | # Augment a list of sentences 11 | sentences = [ 12 | "The video was posted on Facebook by Alex.", 13 | "I plan to run it again this time", 14 | ] 15 | 16 | results = textgenie.magic_lamp( 17 | sentences, "paraphrase: ", n_mask_predictions=5, convert_to_active=True, add_suffix_token=False 18 | ) 19 | 20 | print(results) 21 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | from setuptools import setup 2 | import re 3 | 4 | def get_property(prop, project): 5 | """ 6 | Credits: https://stackoverflow.com/a/41110107 7 | """ 8 | result = re.search(r'{}\s*=\s*[\'"]([^\'"]*)[\'"]'.format(prop), open(project + '/__init__.py').read()) 9 | return result.group(1) 10 | 11 | 12 | with open("README.md", "r") as f: 13 | long_description = f.read() 14 | 15 | setup( 16 | name="textgenie", 17 | version=get_property('__version__', "textgenie"), 18 | description="A python library to augment text data using NLP.", 19 | long_description=long_description, 20 | long_description_content_type="text/markdown", 21 | author="Het Pandya", 22 | url="http://github.com/hetpandya/TextGenie", 23 | author_email="hetpandya6797@gmail.com", 24 | license="MIT", 25 | install_requires=[ 26 | "torch>=1.5.0", 27 | "transformers", 28 | "sentencepiece", 29 | "spacy", 30 | "tqdm", 31 | "pandas", 32 | "Pattern @ git+https://github.com/clips/pattern.git", 33 | ], 34 | packages=["textgenie"], 35 | ) 36 | 37 | -------------------------------------------------------------------------------- /sentences_aug.txt: -------------------------------------------------------------------------------- 1 | Theही was posted on Facebook by Alex. 2 | The video was posted on Facebook by Alex. 3 | The video has been posted by Alex on Facebook. 4 | The attaches was posted on Facebook by Alex. 5 | This video was posted on Facebook by Alex. 6 | The video was posted on Facebook by segregated. 7 | Theanje was posted on Facebook by Alex. 8 | The video was posted to Facebook by Alex. 9 | The video was posted onanje by Alex. 10 | The video was posted on Facebook byihan. 11 | Alex posted the video on Facebook. 12 | The video was posted on Jenelle by Alex. 13 | The video was posted on Facebook by attaches. 14 | The video was posted on attaches by Alex. 15 | The video was posted on합 by Alex. 16 | Theći was posted on Facebook by Alex. 17 | The video was posted in Facebook by Alex. 18 | The video was posted on Facebook by hordes. 19 | The video was posted on Facebook bycardi. 20 | The video was posted onrified by Alex. 21 | The minecraft was posted on Facebook by Alex. 22 | I plan to run next time there this time, going one again? 23 | I want to do it again, no less, in parallel. 24 | So I like him. 25 | I plan to run it again this Tiguan 26 | I plan to run it again this minecraft 27 | I plan to run it again this morgan 28 | I plan to run it again thisgenous 29 | I'll run anything same again tonight. 30 | I plan to run it again this time 31 | I plan to run it again this Jenelle 32 | I planing to run that again: "Farefall”? 33 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | share/python-wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | MANIFEST 28 | 29 | # PyInstaller 30 | # Usually these files are written by a python script from a template 31 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 32 | *.manifest 33 | *.spec 34 | 35 | # Installer logs 36 | pip-log.txt 37 | pip-delete-this-directory.txt 38 | 39 | # Unit test / coverage reports 40 | htmlcov/ 41 | .tox/ 42 | .nox/ 43 | .coverage 44 | .coverage.* 45 | .cache 46 | nosetests.xml 47 | coverage.xml 48 | *.cover 49 | *.py,cover 50 | .hypothesis/ 51 | .pytest_cache/ 52 | cover/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | .pybuilder/ 76 | target/ 77 | 78 | # Jupyter Notebook 79 | .ipynb_checkpoints 80 | 81 | # IPython 82 | profile_default/ 83 | ipython_config.py 84 | 85 | # pyenv 86 | # For a library or package, you might want to ignore these files since the code is 87 | # intended to run in multiple environments; otherwise, check them in: 88 | # .python-version 89 | 90 | # pipenv 91 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 92 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 93 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 94 | # install all needed dependencies. 95 | #Pipfile.lock 96 | 97 | # poetry 98 | # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. 99 | # This is especially recommended for binary packages to ensure reproducibility, and is more 100 | # commonly ignored for libraries. 101 | # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control 102 | #poetry.lock 103 | 104 | # pdm 105 | # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. 106 | #pdm.lock 107 | # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it 108 | # in version control. 109 | # https://pdm.fming.dev/#use-with-ide 110 | .pdm.toml 111 | 112 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm 113 | __pypackages__/ 114 | 115 | # Celery stuff 116 | celerybeat-schedule 117 | celerybeat.pid 118 | 119 | # SageMath parsed files 120 | *.sage.py 121 | 122 | # Environments 123 | .env 124 | .venv 125 | env/ 126 | venv/ 127 | ENV/ 128 | env.bak/ 129 | venv.bak/ 130 | 131 | # Spyder project settings 132 | .spyderproject 133 | .spyproject 134 | 135 | # Rope project settings 136 | .ropeproject 137 | 138 | # mkdocs documentation 139 | /site 140 | 141 | # mypy 142 | .mypy_cache/ 143 | .dmypy.json 144 | dmypy.json 145 | 146 | # Pyre type checker 147 | .pyre/ 148 | 149 | # pytype static type analyzer 150 | .pytype/ 151 | 152 | # Cython debug symbols 153 | cython_debug/ 154 | 155 | # PyCharm 156 | # JetBrains specific template is maintained in a separate JetBrains.gitignore that can 157 | # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore 158 | # and can be added to the global gitignore or merged into this file. For a more nuclear 159 | # option (not recommended) you can uncomment the following to ignore the entire idea folder. 160 | #.idea/ 161 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | [![License](https://img.shields.io/badge/License-MIT-blue.svg)](https://en.wikipedia.org/wiki/MIT_License) 3 | [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) 4 | 5 | Downloads 6 | 7 | 8 |

9 | logo 10 |

11 | 12 | # TextGenie 13 | 14 | TextGenie is a text data augmentations library that helps you augment your text dataset and generate similar kind of samples, thus generating a more robust dataset to train better models. It also takes care of labeled datasets while generating similar samples keeping their labels in memory. 15 | 16 | It uses various Natural Language Processing methods such as paraphrase generation, BERT mask filling and converting text to active voice if found in passive voices. This library currently supports `English` Language. 17 | 18 | ## Installation 19 | ``` 20 | pip install textgenie 21 | ``` 22 | 23 | ## Example 24 | ```python 25 | from textgenie import TextGenie 26 | 27 | textgenie = TextGenie("hetpandya/t5-small-tapaco", "bert-base-uncased") 28 | 29 | # Augment a list of sentences 30 | sentences = [ 31 | "The video was posted on Facebook by Alex.", 32 | "I plan to run it again this time", 33 | ] 34 | textgenie.magic_lamp( 35 | sentences, "paraphrase: ", n_mask_predictions=5, convert_to_active=True 36 | ) 37 | 38 | # Augment data in a txt file 39 | textgenie.magic_lamp( 40 | "sentences.txt", "paraphrase: ", n_mask_predictions=5, convert_to_active=True 41 | ) 42 | 43 | # Augment data in a csv file with labels 44 | textgenie.magic_lamp( 45 | "sentences.csv", 46 | "paraphrase: ", 47 | n_mask_predictions=5, 48 | convert_to_active=True, 49 | label_column="Label", 50 | data_column="Text", 51 | column_names=["Text", "Label"], 52 | ) 53 | ``` 54 | Examples can be found in the examples [notebook](https://github.com/hetpandya/textgenie/blob/main/examples/examples.ipynb). 55 | 56 | ## Usage 57 | 58 | - Initializing the augmentor: 59 | ```textgenie = TextGenie(paraphrase_model_name='model_name',mask_model_name='model_name',spacy_model_name="model_name",device="cpu")``` 60 | - Parameters: 61 | - *paraphrase_model_name*: 62 | - The name of the T5 paraphrase model. 63 | - A list of pretrained model for paraphrase generation can be found [here](https://github.com/hetpandya/paraphrase-datasets-pretrained-models#pretrained-models) 64 | - *mask_model_name*: 65 | - BERT model that will be used to fill masks. This model is disabled by default. But can be enabled by mentioning the name of the BERT model to be used. A list of mask filling models can be found [here](https://huggingface.co/models?filter=en&pipeline_tag=fill-mask) 66 | - *spacy_model_name*: 67 | - Name of the Spacy model. Available models can be found [here](https://spacy.io/models). The default value is set to *en_core_web_sm*. 68 | - *device*: 69 | - The device where the model will be loaded. The default value is set to *cpu*. 70 | - Methods: 71 | - augment_sent_mask_filling(): 72 | - Generate augmented data using BERT mask filling. 73 | - Parameters: 74 | - *sent*: 75 | - The sentence on which augmentation has to be applied. 76 | - *n_mask_predictions*: 77 | - The number of predictions, the BERT mask filling model should generate. The default value is set to *5*. 78 | - augment_sent_t5(): 79 | - Generate augmented data using T5 paraphrasing model. 80 | - Parameters: 81 | - *sent*: 82 | - The sentence on which augmentation has to be applied. 83 | - *prefix*: 84 | - The prefix for the T5 model input. 85 | - *n_predictions*: 86 | - The number of number augmentations, the function should return. The default value is set to *5*. 87 | - *top_k*: 88 | - The number of predictions, the T5 model should generate. The default value is set to *120*. 89 | - *max_length*: 90 | - The max length of the sentence to feed to the model. The default value is set to *256*. 91 | - convert_to_active(): 92 | - Converts a sentence to active voice, if found in passive voice. Otherwise returns the same sentence. 93 | - Parameters: 94 | - *sent*: 95 | - The sentence that has to be converted. 96 | - magic_once(): 97 | - This is a wrapper method for *augment_sent_mask_filling()*, *augment_sent_t5()* and *convert_to_active()* methods. Using this, a sentence can be augmented using all the above mentioned techniques. 98 | - Since this method can operate on individual text data, it can be merged with other packages. 99 | - Parameters: 100 | - *sent*: 101 | - The sentence that has to be augmented. 102 | - *paraphrase_prefix*: 103 | - The prefix for the T5 model input. 104 | - *n_paraphrase_predictions*: 105 | - The number of number augmentations, the function should return. The default value is set to *5*. 106 | - *paraphrase_top_k*: 107 | - The number of predictions, the T5 model should generate. The default value is set to *120*. 108 | - *paraphrase_max_length*: 109 | - The max length of the sentence to feed to the model. The default value is set to *256*. 110 | - *n_mask_predictions*: 111 | - The number of predictions, the BERT mask filling model should generate. The default value is set to *None*. 112 | - *convert_to_active*: 113 | - If the sentence should be converted to active voice. The default value is set to *True*. 114 | - magic_lamp(): 115 | - This method can be used for augmenting whole dataset. Currently accepted dataset formats are: `txt`,`csv`,`tsv` and `list`. 116 | - If the dataset is in `list` or `txt` format, a list of augmented sentences will be returned. Also, a `txt` file with the name *sentences_aug.txt* is saved containing the output of the augmented data. 117 | - If a dataset is in `csv` or `tsv` format with labels, the dataset will be augmented along with keeping in memory the labels for the new samples and a pandas dataframe of the augmented data will be returned. A `tsv` file will be generated with the augmented output with name `original_file_name_aug.tsv` 118 | - Parameters: 119 | - *sentences*: 120 | - The dataset that has to be augmented. This can be a `Python List`, a `txt`, `csv` or `tsv` file. 121 | - *paraphrase_prefix*: 122 | - The prefix for the T5 model input. 123 | - *n_paraphrase_predictions*: 124 | - The number of number augmentations, the function should return. The default value is set to *5*. 125 | - *paraphrase_top_k*: 126 | - The number of predictions, the T5 model should generate. The default value is set to *120*. 127 | - *paraphrase_max_length*: 128 | - The max length of the sentence to feed to the model. The default value is set to *256*. 129 | - *n_mask_predictions*: 130 | - The number of predictions, the BERT mask filling model should generate. The default value is set to *None*. 131 | - *convert_to_active*: 132 | - If the sentence should be converted to active voice. The default value is set to *True*. 133 | - *label_column*: 134 | - The name of the column that contains labeled data. The default value is set to *None*. This parameter is not required to be set if the dataset is in a `Python List` or a `txt` file. 135 | - *data_column*: 136 | - The name of the column that contains data. The default value is set to *None*. This parameter too is not required if the dataset is a `Python List` or a `txt` file. 137 | - *column_names*: 138 | - If the `csv` or `tsv` does not have column names, a Python list has to be passed to give the columns a name. Since this function also accepts `Python List` and a `txt` file, the default value is set to *None*. But, if `csv` or `tsv` files are used, this parameter has to be set. 139 | 140 | 141 | ## References 142 | [Passive To Active](https://github.com/DanManN/pass2act) licensed under the Apache License 2.0 143 | 144 | ## Links 145 | Please find an in depth explanation about the library [on my blog](https://towardsdatascience.com/textgenie-augmenting-your-text-dataset-with-just-2-lines-of-code-23ce883a0715). 146 | 147 | ## License 148 | Please check `LICENSE` for more details. 149 | 150 | -------------------------------------------------------------------------------- /textgenie/textgenie.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from tqdm import tqdm 3 | from .grammar_utils import pass2act, is_passive 4 | from transformers import T5ForConditionalGeneration, T5Tokenizer 5 | from string import punctuation 6 | import os 7 | import pandas as pd 8 | 9 | 10 | def set_seed(seed): 11 | torch.manual_seed(seed) 12 | 13 | 14 | set_seed(42) 15 | 16 | 17 | class TextGenie: 18 | def __init__( 19 | self, 20 | paraphrase_model_name, 21 | mask_model_name=None, 22 | spacy_model_name="en_core_web_sm", 23 | device="cpu", 24 | ): 25 | tqdm.write("Loading Paraphrase Model..") 26 | self.paraphrase_model = T5ForConditionalGeneration.from_pretrained( 27 | paraphrase_model_name 28 | ) 29 | self.paraphrase_tokenizer = T5Tokenizer.from_pretrained(paraphrase_model_name) 30 | self.paraphrase_model = self.paraphrase_model.to(device) 31 | self.device = device 32 | 33 | import spacy 34 | self.nlp = spacy.load(spacy_model_name) 35 | 36 | if mask_model_name: 37 | tqdm.write("Loading Mask Fill Model..") 38 | from transformers import pipeline 39 | from string import punctuation 40 | 41 | self.mask_augmenter = pipeline("fill-mask", model=mask_model_name) 42 | 43 | def extract_keywords(self, sentence): 44 | result = [] 45 | pos_tag = ["PROPN", "NOUN", "ADJ"] 46 | consider_tags = ["NUM"] 47 | pos_tag = pos_tag + consider_tags 48 | 49 | doc = self.nlp(sentence) 50 | 51 | for token in doc: 52 | if ( 53 | token.text in self.nlp.Defaults.stop_words or token.text in punctuation 54 | ) and token.pos_ not in consider_tags: 55 | continue 56 | if token.pos_ in pos_tag: 57 | result.append(token.text) 58 | return list(set(result)) 59 | 60 | def augment_sent_mask_filling(self, sent, n_mask_predictions=5): 61 | keywords = self.extract_keywords(sent) 62 | augmented_sents = [] 63 | for keyword in keywords: 64 | masked_sent = sent.replace(keyword, self.mask_augmenter.tokenizer.mask_token, 1) 65 | augmented_sents.extend( 66 | [ 67 | generated_sent["sequence"] 68 | for generated_sent in self.mask_augmenter( 69 | masked_sent, top_k=n_mask_predictions 70 | ) 71 | if generated_sent["sequence"].lower() != sent.lower() 72 | ] 73 | ) 74 | return augmented_sents 75 | 76 | def augment_sent_t5(self, sent, prefix, n_predictions=5, top_k=120, max_length=256, add_suffix_token=True): 77 | text = prefix + sent + " " if add_suffix_token else "" 78 | encoding = self.paraphrase_tokenizer.encode_plus( 79 | text, pad_to_max_length=True, return_tensors="pt" 80 | ) 81 | input_ids, attention_masks = encoding["input_ids"].to(self.device), encoding[ 82 | "attention_mask" 83 | ].to(self.device) 84 | 85 | beam_outputs = self.paraphrase_model.generate( 86 | input_ids=input_ids, 87 | attention_mask=attention_masks, 88 | do_sample=True, 89 | max_length=max_length, 90 | top_k=top_k, 91 | top_p=0.98, 92 | early_stopping=True, 93 | num_return_sequences=n_predictions, 94 | ) 95 | 96 | final_outputs = [] 97 | for beam_output in beam_outputs: 98 | generated_sent = self.paraphrase_tokenizer.decode( 99 | beam_output, skip_special_tokens=True, clean_up_tokenization_spaces=True 100 | ) 101 | if ( 102 | generated_sent.lower() != sent.lower() 103 | and generated_sent not in final_outputs 104 | ): 105 | final_outputs.append(generated_sent) 106 | return final_outputs 107 | 108 | def convert_to_active(self, sent): 109 | if is_passive(sent, nlp=self.nlp): 110 | return pass2act(sent, nlp=self.nlp) 111 | else: 112 | return sent 113 | 114 | def magic_once( 115 | self, 116 | sent, 117 | paraphrase_prefix, 118 | n_paraphrase_predictions=5, 119 | paraphrase_top_k=120, 120 | paraphrase_max_length=256, 121 | n_mask_predictions=None, 122 | convert_to_active=True, 123 | add_suffix_token=True, 124 | ): 125 | sent = sent.strip() 126 | output = [] 127 | output.append(sent) 128 | output += self.augment_sent_t5( 129 | sent, 130 | paraphrase_prefix, 131 | n_paraphrase_predictions, 132 | paraphrase_top_k, 133 | paraphrase_max_length, 134 | ) 135 | if n_mask_predictions and isinstance(n_mask_predictions, int): 136 | output += self.augment_sent_mask_filling(sent, n_mask_predictions) 137 | if convert_to_active: 138 | active_voice = self.convert_to_active(sent) 139 | if active_voice.lower() != sent.lower(): 140 | output.append(active_voice) 141 | return list(set(output)) 142 | 143 | def magic_lamp( 144 | self, 145 | sentences, 146 | paraphrase_prefix, 147 | n_paraphrase_predictions=5, 148 | paraphrase_top_k=120, 149 | paraphrase_max_length=256, 150 | n_mask_predictions=None, 151 | convert_to_active=True, 152 | label_column=None, 153 | data_column=None, 154 | column_names=None, 155 | add_suffix_token=True, 156 | ): 157 | all_sentences = None 158 | with_labels = False 159 | out_file = os.path.join(os.getcwd(), "sentences_aug.txt") 160 | 161 | if isinstance(sentences, str): 162 | sentences = os.path.join(os.getcwd(), sentences) 163 | if sentences.endswith(".txt"): 164 | all_sentences = open(sentences).read().strip().split("\n") 165 | elif sentences.endswith(".csv") or sentences.endswith(".tsv"): 166 | if not label_column: 167 | raise Exception( 168 | "Please provide the column name that contains labels using the 'label_column' parameter." 169 | ) 170 | if not data_column: 171 | raise Exception( 172 | "Please provide the column name that contains data using the 'data_column' parameter." 173 | ) 174 | if column_names and not isinstance(column_names, list): 175 | raise Exception("Please provide column names in a python list.") 176 | out_file = ( 177 | sentences.replace(".csv", "").replace(".tsv", "") + "_aug.tsv" 178 | ) 179 | with_labels = True 180 | if sentences.endswith(".csv"): 181 | if column_names: 182 | all_sentences = pd.read_csv(sentences, names=column_names) 183 | else: 184 | all_sentences = pd.read_csv(sentences) 185 | elif sentences.endswith(".tsv"): 186 | if column_names: 187 | all_sentences = pd.read_csv( 188 | sentences, names=column_names, sep="\t" 189 | ) 190 | else: 191 | all_sentences = pd.read_csv(sentences) 192 | if label_column not in all_sentences.columns: 193 | raise Exception( 194 | "Please provide label column name for the dataset using the 'label_column' parameter. If already provided, please check for typos in the name of the label column." 195 | ) 196 | if data_column not in all_sentences.columns: 197 | raise Exception( 198 | "Please provide data column name for the dataset using the 'data_column' parameter. If already provided, please check for typos in the name of the data column." 199 | ) 200 | labels = all_sentences[label_column].unique() 201 | 202 | if all_sentences.iloc[0].tolist() == column_names: 203 | all_sentences = all_sentences.drop(0) 204 | augmented_data = [] 205 | 206 | for ix in tqdm(range(all_sentences.shape[0])): 207 | sent = all_sentences[data_column][ix].strip() 208 | label = all_sentences[label_column][ix].strip() 209 | aug_sent = self.magic_once( 210 | sent, 211 | paraphrase_prefix, 212 | n_paraphrase_predictions, 213 | paraphrase_top_k, 214 | paraphrase_max_length, 215 | n_mask_predictions, 216 | convert_to_active, 217 | add_suffix_token, 218 | ) 219 | aug_sent = [[s, label] for s in aug_sent] 220 | augmented_data.extend(aug_sent) 221 | augmented_data = pd.DataFrame( 222 | data=augmented_data, columns=["Text", "Label"] 223 | ) 224 | augmented_data.to_csv(out_file, sep="\t", index=None) 225 | else: 226 | raise Exception( 227 | "Unsupported file format. Currently, following formats are supported: list/csv/tsv" 228 | ) 229 | elif isinstance(sentences, list): 230 | all_sentences = sentences 231 | if all_sentences is None: 232 | raise Exception("Error: No sentences found.") 233 | if not with_labels: 234 | augmented_data = [] 235 | for sent in tqdm(all_sentences): 236 | augmented_data.extend( 237 | self.magic_once( 238 | sent, 239 | paraphrase_prefix, 240 | n_paraphrase_predictions, 241 | paraphrase_top_k, 242 | paraphrase_max_length, 243 | n_mask_predictions, 244 | convert_to_active, 245 | add_suffix_token, 246 | ) 247 | ) 248 | with open(out_file, "w") as f: 249 | for line in augmented_data: 250 | f.write(line + "\n") 251 | tqdm.write(f"\nCompleted writing output to {out_file}.") 252 | return augmented_data 253 | -------------------------------------------------------------------------------- /textgenie/grammar_utils.py: -------------------------------------------------------------------------------- 1 | import spacy 2 | from spacy.matcher import Matcher 3 | 4 | try: 5 | import pattern.en as en 6 | from pattern.en import lexeme 7 | except LookupError: 8 | import nltk 9 | 10 | nltk.download("omw-1.4") 11 | import pattern.en as en 12 | from pattern.en import lexeme 13 | 14 | noundict = { 15 | "i": "me", 16 | "we": "us", 17 | "you": "you", 18 | "he": "him", 19 | "she": "her", 20 | "they": "them", 21 | "them": "they", 22 | "her": "she", 23 | "him": "he", 24 | "us": "we", 25 | "me": "i", 26 | } 27 | 28 | 29 | def nouninv(noun): 30 | n = noun.lower() 31 | if n in noundict: 32 | return noundict[n] 33 | return noun 34 | 35 | 36 | def pattern_stopiteration_workaround(): 37 | try: 38 | print(lexeme("check")) 39 | except: 40 | pass 41 | 42 | 43 | pattern_stopiteration_workaround() 44 | 45 | 46 | def pass2act(doc, nlp, rec=False): 47 | """ 48 | Author : Daniel Nohimovich & Zhekai Jin (Scott) 49 | Course : ECE 467 Natural Language Processing 50 | Instructor : Professor Carl Sable 51 | https://github.com/DanManN/pass2act 52 | All rights reserved by the orignal author. 53 | This source code is licensed under the Apache License 2.0 found in the 54 | LICENSE file in the root directory of this source tree. 55 | """ 56 | parse = nlp(doc) 57 | newdoc = "" 58 | for sent in parse.sents: 59 | 60 | # Init parts of sentence to capture: 61 | subjpass = "" 62 | subj = "" 63 | verb = "" 64 | verbaspect = "" 65 | verbtense = "" 66 | adverb = {"bef": "", "aft": ""} 67 | part = "" 68 | prep = "" 69 | agent = "" 70 | aplural = False 71 | advcltree = None 72 | aux = list(list(nlp(". .").sents)[0]) # start with 2 'null' elements 73 | xcomp = "" 74 | punc = "." 75 | # Analyse dependency tree: 76 | for word in sent: 77 | if word.dep_ == "advcl": 78 | if word.head.dep_ in ("ROOT", "auxpass"): 79 | advcltree = word.subtree 80 | if word.dep_ == "nsubjpass": 81 | if word.head.dep_ == "ROOT": 82 | subjpass = "".join( 83 | w.text_with_ws.lower() 84 | if w.tag_ not in ("NNP", "NNPS") 85 | else w.text_with_ws 86 | for w in word.subtree 87 | ).strip() 88 | if word.dep_ == "nsubj": 89 | subj = "".join( 90 | w.text_with_ws.lower() 91 | if w.tag_ not in ("NNP", "NNPS") 92 | else w.text_with_ws 93 | for w in word.subtree 94 | ).strip() 95 | if word.head.dep_ == "auxpass": 96 | if word.head.head.dep_ == "ROOT": 97 | subjpass = subj 98 | if word.dep_ in ("advmod", "npadvmod", "oprd"): 99 | if word.head.dep_ == "ROOT": 100 | if verb == "": 101 | adverb["bef"] = "".join( 102 | w.text_with_ws.lower() 103 | if w.tag_ not in ("NNP", "NNPS") 104 | else w.text_with_ws 105 | for w in word.subtree 106 | ).strip() 107 | else: 108 | adverb["aft"] = "".join( 109 | w.text_with_ws.lower() 110 | if w.tag_ not in ("NNP", "NNPS") 111 | else w.text_with_ws 112 | for w in word.subtree 113 | ).strip() 114 | if word.dep_ == "auxpass": 115 | if word.head.dep_ == "ROOT": 116 | if not subjpass: 117 | subjpass = subj 118 | if word.dep_ in ("aux", "auxpass", "neg"): 119 | if word.head.dep_ == "ROOT": 120 | aux += [word] 121 | if word.dep_ == "ROOT": 122 | verb = word.text 123 | if word.tag_ == "VB": 124 | verbtense = en.INFINITIVE 125 | elif word.tag_ == "VBD": 126 | verbtense = en.PAST 127 | elif word.tag_ == "VBG": 128 | verbtense = en.PRESENT 129 | verbaspect = en.PROGRESSIVE 130 | elif word.tag_ == "VBN": 131 | verbtense = en.PAST 132 | else: 133 | verbtense = en.tenses(word.text)[0][0] 134 | if word.dep_ == "prt": 135 | if word.head.dep_ == "ROOT": 136 | part = "".join( 137 | w.text_with_ws.lower() 138 | if w.tag_ not in ("NNP", "NNPS") 139 | else w.text_with_ws 140 | for w in word.subtree 141 | ).strip() 142 | if word.dep_ == "prep": 143 | if word.head.dep_ == "ROOT": 144 | prep = "".join( 145 | w.text_with_ws.lower() 146 | if w.tag_ not in ("NNP", "NNPS") 147 | else w.text_with_ws 148 | for w in word.subtree 149 | ).strip() 150 | if word.dep_.endswith("obj"): 151 | if word.head.dep_ == "agent": 152 | if word.head.head.dep_ == "ROOT": 153 | agent = "".join( 154 | w.text + ", " 155 | if w.dep_ == "appos" 156 | else ( 157 | w.text_with_ws.lower() 158 | if w.tag_ not in ("NNP", "NNPS") 159 | else w.text_with_ws 160 | ) 161 | for w in word.subtree 162 | ).strip() 163 | aplural = word.tag_ in ("NNS", "NNPS") 164 | if word.dep_ in ("xcomp", "ccomp", "conj"): 165 | if word.head.dep_ == "ROOT": 166 | xcomp = "".join( 167 | w.text_with_ws.lower() 168 | if w.tag_ not in ("NNP", "NNPS") 169 | else w.text_with_ws 170 | for w in word.subtree 171 | ).strip() 172 | that = xcomp.startswith("that") 173 | xcomp = pass2act(xcomp, True).strip(" .") 174 | if not xcomp.startswith("that") and that: 175 | xcomp = "that " + xcomp 176 | if word.dep_ == "punct" and not rec: 177 | if word.text != '"': 178 | punc = word.text 179 | 180 | # exit if not passive: 181 | if subjpass == "": 182 | newdoc += str(sent) + " " 183 | continue 184 | 185 | # if no agent is found: 186 | if agent == "": 187 | # what am I gonna do? BITconEEEEEEECT!!!! 188 | newdoc += str(sent) + " " 189 | continue 190 | 191 | # invert nouns: 192 | agent = nouninv(agent) 193 | subjpass = nouninv(subjpass) 194 | 195 | # FUCKING CONJUGATION!!!!!!!!!!!!!: 196 | auxstr = "" 197 | num = en.SINGULAR if not aplural or agent in ("he", "she") else en.PLURAL 198 | aux.append(aux[0]) 199 | verbaspect = None 200 | for (pp, p, a, n) in zip(aux, aux[1:], aux[2:], aux[3:]): 201 | if a.lemma_ == ".": 202 | continue 203 | 204 | if a.lemma_ == "not": 205 | if p.lemma_ == "be": 206 | if n.lemma_ == "be": 207 | verbtense = en.tenses(a.text)[0][0] 208 | auxstr += ( 209 | en.conjugate( 210 | "be", tense=en.tenses(p.text)[0][0], number=num 211 | ) 212 | + " " 213 | ) 214 | verbaspect = en.PROGRESSIVE 215 | else: 216 | auxstr += ( 217 | en.conjugate( 218 | "do", tense=en.tenses(p.text)[0][0], number=num 219 | ) 220 | + " " 221 | ) 222 | verbtense = en.INFINITIVE 223 | auxstr += "not " 224 | elif a.lemma_ == "be": 225 | if p.lemma_ == "be": 226 | verbtense = en.tenses(a.text)[0][0] 227 | auxstr += ( 228 | en.conjugate("be", tense=en.tenses(a.text)[0][0], number=num) 229 | + " " 230 | ) 231 | verbaspect = en.PROGRESSIVE 232 | elif p.tag_ == "MD": 233 | verbtense = en.INFINITIVE 234 | elif a.lemma_ == "have": 235 | num == en.PLURAL if p.tag_ == "MD" else num 236 | auxstr += ( 237 | en.conjugate("have", tense=en.tenses(a.text)[0][0], number=num) 238 | + " " 239 | ) 240 | if n.lemma_ == "be": 241 | verbaspect = en.PROGRESSIVE 242 | verbtense = en.tenses(n.text)[0][0] 243 | else: 244 | auxstr += a.text_with_ws 245 | auxstr = auxstr.lower().strip() 246 | 247 | if verbaspect: 248 | verb = en.conjugate(verb, tense=verbtense, aspect=verbaspect) 249 | else: 250 | verb = en.conjugate(verb, tense=verbtense) 251 | 252 | advcl = "" 253 | if advcltree: 254 | for w in advcltree: 255 | if w.pos_ == "VERB" and en.tenses(w.text)[0][4] == en.PROGRESSIVE: 256 | advcl += ( 257 | "which " 258 | + en.conjugate(w.text, tense=en.tenses(verb)[0][0]) 259 | + " " 260 | ) 261 | else: 262 | advcl += w.text_with_ws 263 | 264 | newsent = ( 265 | " ".join( 266 | list( 267 | filter( 268 | None, 269 | [ 270 | agent, 271 | auxstr, 272 | adverb["bef"], 273 | verb, 274 | part, 275 | subjpass, 276 | adverb["aft"], 277 | advcl, 278 | prep, 279 | xcomp, 280 | ], 281 | ) 282 | ) 283 | ) 284 | + punc 285 | ) 286 | if not rec: 287 | newsent = newsent[0].upper() + newsent[1:] 288 | newdoc += newsent + " " 289 | return newdoc 290 | 291 | 292 | def is_passive(sentence, nlp): 293 | doc = nlp(sentence) 294 | passive_rule = [ 295 | {"DEP": "nsubjpass"}, 296 | {"DEP": "aux", "OP": "*"}, 297 | {"DEP": "auxpass"}, 298 | {"TAG": "VBN"}, 299 | ] 300 | 301 | matcher = Matcher(nlp.vocab) 302 | 303 | matcher.add("Passive", [passive_rule]) 304 | matches = matcher(doc) 305 | if matches: 306 | return True 307 | else: 308 | return False 309 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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-------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "name": "textgenie-examples.ipynb", 7 | "provenance": [], 8 | "collapsed_sections": [] 9 | }, 10 | "kernelspec": { 11 | "name": "python3", 12 | "display_name": "Python 3" 13 | }, 14 | "language_info": { 15 | "name": "python" 16 | } 17 | }, 18 | "cells": [ 19 | { 20 | "cell_type": "code", 21 | "metadata": { 22 | "colab": { 23 | "base_uri": "https://localhost:8080/", 24 | "height": 1000 25 | }, 26 | "id": "4b69iyum-FxW", 27 | "outputId": "8fb63125-6ac3-4fc8-c4c1-20fcfea72e31" 28 | }, 29 | "source": [ 30 | "!pip install --upgrade textgenie" 31 | ], 32 | "execution_count": 10, 33 | "outputs": [ 34 | { 35 | "output_type": "stream", 36 | "text": [ 37 | "Collecting git+https://github.com/hetpandya/textgenie.git\n", 38 | " Cloning https://github.com/hetpandya/textgenie.git to /tmp/pip-req-build-42y38bmw\n", 39 | " Running command git clone -q https://github.com/hetpandya/textgenie.git /tmp/pip-req-build-42y38bmw\n", 40 | "Requirement already satisfied, skipping upgrade: torch>=1.5.0 in /usr/local/lib/python3.7/dist-packages (from textgenie==0.1.2) (1.9.0+cu102)\n", 41 | "Requirement already satisfied, skipping upgrade: transformers in /usr/local/lib/python3.7/dist-packages (from textgenie==0.1.2) (4.7.0)\n", 42 | "Requirement already satisfied, skipping upgrade: sentencepiece in /usr/local/lib/python3.7/dist-packages (from textgenie==0.1.2) (0.1.96)\n", 43 | "Requirement already satisfied, skipping upgrade: spacy in /usr/local/lib/python3.7/dist-packages (from textgenie==0.1.2) (2.2.4)\n", 44 | "Requirement already satisfied, skipping upgrade: tqdm in /usr/local/lib/python3.7/dist-packages (from textgenie==0.1.2) (4.41.1)\n", 45 | "Requirement already satisfied, skipping upgrade: pattern in /usr/local/lib/python3.7/dist-packages (from textgenie==0.1.2) (3.6)\n", 46 | "Requirement already satisfied, skipping upgrade: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.5.0->textgenie==0.1.2) (3.7.4.3)\n", 47 | "Requirement already satisfied, skipping upgrade: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers->textgenie==0.1.2) (2019.12.20)\n", 48 | "Requirement already satisfied, skipping upgrade: requests in /usr/local/lib/python3.7/dist-packages (from transformers->textgenie==0.1.2) (2.23.0)\n", 49 | "Requirement already satisfied, skipping upgrade: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers->textgenie==0.1.2) (1.19.5)\n", 50 | "Requirement already satisfied, skipping upgrade: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from transformers->textgenie==0.1.2) (4.5.0)\n", 51 | "Requirement already satisfied, skipping upgrade: filelock in /usr/local/lib/python3.7/dist-packages (from transformers->textgenie==0.1.2) (3.0.12)\n", 52 | "Requirement already satisfied, skipping upgrade: huggingface-hub==0.0.8 in /usr/local/lib/python3.7/dist-packages (from transformers->textgenie==0.1.2) (0.0.8)\n", 53 | "Requirement already satisfied, skipping upgrade: tokenizers<0.11,>=0.10.1 in /usr/local/lib/python3.7/dist-packages (from transformers->textgenie==0.1.2) (0.10.3)\n", 54 | "Requirement already satisfied, skipping upgrade: pyyaml in /usr/local/lib/python3.7/dist-packages (from transformers->textgenie==0.1.2) (3.13)\n", 55 | "Requirement already satisfied, skipping upgrade: packaging in /usr/local/lib/python3.7/dist-packages (from transformers->textgenie==0.1.2) (20.9)\n", 56 | "Requirement already satisfied, skipping upgrade: sacremoses in /usr/local/lib/python3.7/dist-packages (from transformers->textgenie==0.1.2) (0.0.45)\n", 57 | "Requirement already satisfied, skipping upgrade: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.7/dist-packages (from spacy->textgenie==0.1.2) (1.1.3)\n", 58 | "Requirement already satisfied, skipping upgrade: 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satisfied, skipping upgrade: pytz in /usr/local/lib/python3.7/dist-packages (from tempora>=1.8->portend>=2.1.1->cherrypy->pattern->textgenie==0.1.2) (2018.9)\n", 101 | "Requirement already satisfied, skipping upgrade: pycparser in /usr/local/lib/python3.7/dist-packages (from cffi>=1.12->cryptography->pdfminer.six->pattern->textgenie==0.1.2) (2.20)\n", 102 | "Building wheels for collected packages: textgenie\n", 103 | " Building wheel for textgenie (setup.py) ... \u001b[?25l\u001b[?25hdone\n", 104 | " Created wheel for textgenie: filename=textgenie-0.1.2-cp37-none-any.whl size=8705 sha256=f8cb665c1c8c93f7792b85d66124020d326625f21d43873fb6d0db71f67437af\n", 105 | " Stored in directory: /tmp/pip-ephem-wheel-cache-8giac7ls/wheels/35/24/87/4f20f5d3fa823cf98bf2d27bb95281c19c3436f82888aa6adc\n", 106 | "Successfully built textgenie\n", 107 | "Installing collected packages: textgenie\n", 108 | " Found existing installation: textgenie 0.1.1\n", 109 | " Uninstalling textgenie-0.1.1:\n", 110 | " Successfully uninstalled textgenie-0.1.1\n", 111 | "Successfully installed textgenie-0.1.2\n" 112 | ], 113 | "name": "stdout" 114 | }, 115 | { 116 | "output_type": "display_data", 117 | "data": { 118 | "application/vnd.colab-display-data+json": { 119 | "pip_warning": { 120 | "packages": [ 121 | "textgenie" 122 | ] 123 | } 124 | } 125 | }, 126 | "metadata": { 127 | "tags": [] 128 | } 129 | } 130 | ] 131 | }, 132 | { 133 | "cell_type": "code", 134 | "metadata": { 135 | "id": "_h3lGuIV-KKI" 136 | }, 137 | "source": [ 138 | "from textgenie import TextGenie" 139 | ], 140 | "execution_count": 1, 141 | "outputs": [] 142 | }, 143 | { 144 | "cell_type": "code", 145 | "metadata": { 146 | "colab": { 147 | "base_uri": "https://localhost:8080/" 148 | }, 149 | "id": "Tg2igVb6-UxL", 150 | "outputId": "790b4af8-9eb8-42ed-cf01-cb9cb5555e72" 151 | }, 152 | "source": [ 153 | "textgenie = TextGenie(\"ramsrigouthamg/t5_paraphraser\",'bert-base-uncased')" 154 | ], 155 | "execution_count": 2, 156 | "outputs": [ 157 | { 158 | "output_type": "stream", 159 | "text": [ 160 | "Loading Paraphrase Model..\n", 161 | "Loading Mask Fill Model..\n" 162 | ], 163 | "name": "stdout" 164 | }, 165 | { 166 | "output_type": "stream", 167 | "text": [ 168 | "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForMaskedLM: ['cls.seq_relationship.bias', 'cls.seq_relationship.weight']\n", 169 | "- This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", 170 | "- This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n" 171 | ], 172 | "name": "stderr" 173 | } 174 | ] 175 | }, 176 | { 177 | "cell_type": "code", 178 | "metadata": { 179 | "colab": { 180 | "base_uri": "https://localhost:8080/" 181 | }, 182 | "id": "P06v8CkK-vHu", 183 | "outputId": "bec9a13a-58cd-4f91-bf1c-f2f652fff9e5" 184 | }, 185 | "source": [ 186 | "# Augment a list of sentences\n", 187 | "sentences = [\"The video was posted on Facebook by Alex.\",\"I plan to run it again this time\"]\n", 188 | "textgenie.magic_lamp(sentences,\"paraphrase: \",n_paraphrase_predictions=15,n_mask_predictions=15,convert_to_active=True)" 189 | ], 190 | "execution_count": 5, 191 | "outputs": [ 192 | { 193 | "output_type": "stream", 194 | "text": [ 195 | "\r 0%| | 0/2 [00:00. In future versions this behavior may lead to duplicated eos tokens being added.\n", 198 | " f\"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated eos tokens being added.\"\n", 199 | "100%|██████████| 2/2 [00:18<00:00, 9.20s/it]" 200 | ], 201 | "name": "stderr" 202 | }, 203 | { 204 | "output_type": "stream", 205 | "text": [ 206 | "\n", 207 | "Completed writing output to /content/sentences_aug.txt.\n" 208 | ], 209 | "name": "stdout" 210 | }, 211 | { 212 | "output_type": "stream", 213 | "text": [ 214 | "\n" 215 | ], 216 | "name": "stderr" 217 | }, 218 | { 219 | "output_type": "execute_result", 220 | "data": { 221 | "text/plain": [ 222 | "['the clip was posted on facebook by alex.',\n", 223 | " 'the video was posted on facebook by youtube.',\n", 224 | " 'the event was posted on facebook by alex.',\n", 225 | " 'the text was posted on facebook by alex.',\n", 226 | " 'the cover was posted on facebook by alex.',\n", 227 | " 'the story was posted on facebook by alex.',\n", 228 | " 'the article was posted on facebook by alex.',\n", 229 | " 'the film was posted on facebook by alex.',\n", 230 | " 'the video was posted on facebook by twitter.',\n", 231 | " 'Is it true that the video, posted in Facebook, was created by Alex?',\n", 232 | " 'the photo was posted on facebook by alex.',\n", 233 | " 'What videos have you seen on FaceBook (Alex)?',\n", 234 | " 'If I could capture this on Facebook, what would be the point of posting an Instagram video?',\n", 235 | " 'I just saw the video posted on Facebook by Alex Wenzel. This will impact to tell you more than we realise.',\n", 236 | " 'the video was posted on blogs by alex.',\n", 237 | " 'the video was posted on facebook by mtv.',\n", 238 | " 'the video was posted on youtube by alex.',\n", 239 | " 'the video was posted on tv by alex.',\n", 240 | " 'the video was posted on facebook by anonymous.',\n", 241 | " 'the single was posted on facebook by alex.',\n", 242 | " 'the video was posted on vine by alex.',\n", 243 | " 'the video was posted on facebook by rihanna.',\n", 244 | " 'the video was posted on facebook by members.',\n", 245 | " 'the video was posted on twitch by alex.',\n", 246 | " 'the video was posted on facebook by others.',\n", 247 | " 'the video was posted on amazon by alex.',\n", 248 | " 'the video was posted on facebook by fans.',\n", 249 | " 'Alex posted the video on Facebook. ',\n", 250 | " 'The video was posted on Facebook by Alex Pease.',\n", 251 | " 'the video was posted on facebook by fox.',\n", 252 | " 'the video was posted on facebook by her.',\n", 253 | " 'the video was posted on google by alex.',\n", 254 | " 'the video was posted on facebook by himself.',\n", 255 | " 'the song was posted on facebook by alex.',\n", 256 | " 'the video was posted on itunes by alex.',\n", 257 | " 'the video was posted on video by alex.',\n", 258 | " 'the video was posted on twitter by alex.',\n", 259 | " 'the video was posted on facebook by rt.',\n", 260 | " 'the video was posted on myspace by alex.',\n", 261 | " 'the video was posted on site by alex.',\n", 262 | " 'the trailer was posted on facebook by alex.',\n", 263 | " \"Watch Alex Rodriguez' Facebook video here.\",\n", 264 | " 'the video was posted on facebook by friends.',\n", 265 | " \"This video was posted on Facebook by Alex. I've been using a mobile app for the past few days but just cant seem to find the time to download it.\",\n", 266 | " 'the track was posted on facebook by alex.',\n", 267 | " 'the video was posted on mtv by alex.',\n", 268 | " 'the album was posted on facebook by alex.',\n", 269 | " 'This is a viral video I uploaded to Facebook and showed on my Facebook profile.',\n", 270 | " 'The video was posted on Facebook by Alex.',\n", 271 | " 'the announcement was posted on facebook by alex.',\n", 272 | " 'the video was posted on facebook by them.',\n", 273 | " 'the video was posted on internet by alex.',\n", 274 | " 'the video was posted on facebook by him.',\n", 275 | " 'i plan to run it again this month',\n", 276 | " 'i plan to run it again this.',\n", 277 | " 'i plan to run it again this week',\n", 278 | " 'I plan to run it again this time this time.',\n", 279 | " 'Is it possible to run it again after it starts if you want it again?',\n", 280 | " 'I plan to run it again this time this time this time I know the plot. I guess they will be able to continue to run it then.',\n", 281 | " 'i plan to run it again this ;',\n", 282 | " \"I plan to run this again this time this time around. I'll be writing more frequently than I have the time and the plan is much less complex.\",\n", 283 | " 'I plan to run it again this time this time this time.',\n", 284 | " \"I plan to run it again this time this time this time. I'm sure that I'll be able to find a runner again.\",\n", 285 | " 'I plan to run it again this time',\n", 286 | " \"I plan to run it again this time this time again this time I can't remember whether I really needed to keep it running but its good enough.\",\n", 287 | " 'I will run it again. I plan to run it again this time.',\n", 288 | " \"I'll run it now again but the second time I've completed.\",\n", 289 | " 'I plan to run it again this time this time. It actually helps me. So, I will run it again.',\n", 290 | " 'i plan to run it again this day',\n", 291 | " 'i plan to run it again this...',\n", 292 | " 'i plan to run it again this year',\n", 293 | " 'i plan to run it again this summer',\n", 294 | " 'What is the plan to run it again?',\n", 295 | " 'I plan to run It again this time now this time in Linux.',\n", 296 | " 'How is this book going to be run again?',\n", 297 | " 'i plan to run it again this season',\n", 298 | " \"I plan to run it again this time I'm not going to run it again this time. If I didn't run it, I don't expect to miss out on it.\",\n", 299 | " 'i plan to run it again this weekend',\n", 300 | " 'i plan to run it again this!',\n", 301 | " 'i plan to run it again this semester',\n", 302 | " 'i plan to run it again this?',\n", 303 | " 'i plan to run it again this morning',\n", 304 | " 'I can always run it again, I just want to try putting it back. This time a fortnight after.']" 305 | ] 306 | }, 307 | "metadata": { 308 | "tags": [] 309 | }, 310 | "execution_count": 5 311 | } 312 | ] 313 | }, 314 | { 315 | "cell_type": "code", 316 | "metadata": { 317 | "colab": { 318 | "base_uri": "https://localhost:8080/" 319 | }, 320 | "id": "fN8rA8bc-cF1", 321 | "outputId": "93d4aafb-5027-4c13-9f49-c7a54777cc7e" 322 | }, 323 | "source": [ 324 | "%%writefile sentences.txt\n", 325 | "At dinner, six shrimp were eaten by Harry.\n", 326 | "Beautiful giraffes roam the savannah." 327 | ], 328 | "execution_count": 6, 329 | "outputs": [ 330 | { 331 | "output_type": "stream", 332 | "text": [ 333 | "Writing sentences.txt\n" 334 | ], 335 | "name": "stdout" 336 | } 337 | ] 338 | }, 339 | { 340 | "cell_type": "code", 341 | "metadata": { 342 | "colab": { 343 | "base_uri": "https://localhost:8080/" 344 | }, 345 | "id": "TQLNyJyH-t_T", 346 | "outputId": "f3765c08-2fd7-4b3f-bc33-6413fe94e4c4" 347 | }, 348 | "source": [ 349 | "# Augment data in a txt file\n", 350 | "textgenie.magic_lamp(\"sentences.txt\",\"paraphrase: \",n_mask_predictions=5,convert_to_active=True)" 351 | ], 352 | "execution_count": 7, 353 | "outputs": [ 354 | { 355 | "output_type": "stream", 356 | "text": [ 357 | "\r 0%| | 0/2 [00:00. In future versions this behavior may lead to duplicated eos tokens being added.\n", 360 | " f\"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated eos tokens being added.\"\n", 361 | "100%|██████████| 2/2 [00:07<00:00, 3.70s/it]" 362 | ], 363 | "name": "stderr" 364 | }, 365 | { 366 | "output_type": "stream", 367 | "text": [ 368 | "\n", 369 | "Completed writing output to /content/sentences_aug.txt.\n" 370 | ], 371 | "name": "stdout" 372 | }, 373 | { 374 | "output_type": "stream", 375 | "text": [ 376 | "\n" 377 | ], 378 | "name": "stderr" 379 | }, 380 | { 381 | "output_type": "execute_result", 382 | "data": { 383 | "text/plain": [ 384 | "['at lunch, six shrimp were eaten by harry.',\n", 385 | " 'at dinner, six shrimp were eaten by hand.',\n", 386 | " 'at night, six shrimp were eaten by harry.',\n", 387 | " 'At dinner, Harry was having 6 shrimps.',\n", 388 | " 'At dinner, Harry ate six shrimp.',\n", 389 | " 'at least, six shrimp were eaten by harry.',\n", 390 | " 'at dinner, six shrimp were eaten by him.',\n", 391 | " 'at dinner, six shrimp were eaten by chicken.',\n", 392 | " 'at dinner, six pancakes were eaten by harry.',\n", 393 | " 'at dinner, six shrimp were eaten by everyone.',\n", 394 | " 'at dinner, his shrimp were eaten by harry.',\n", 395 | " \"During Harry's dinner, he ate eight shrimp.\",\n", 396 | " 'at dinner, her shrimp were eaten by harry.',\n", 397 | " 'at dinner, these shrimp were eaten by harry.',\n", 398 | " 'at dinner, six eggs were eaten by harry.',\n", 399 | " 'Harry ate six shrimp at dinner. ',\n", 400 | " 'Harry ate six shrimp.',\n", 401 | " 'at first, six shrimp were eaten by harry.',\n", 402 | " 'at dinner, the shrimp were eaten by harry.',\n", 403 | " 'at dinner, some shrimp were eaten by harry.',\n", 404 | " 'at dinner, six sandwiches were eaten by harry.',\n", 405 | " \"During Harry's dinner, six shrimp were eaten by Harry.\",\n", 406 | " 'At dinner, six shrimp were eaten by Harry.',\n", 407 | " 'at dinner, six dishes were eaten by harry.',\n", 408 | " 'at dinner, six meals were eaten by harry.',\n", 409 | " 'at dinner, six shrimp were eaten by themselves.',\n", 410 | " 'How many beautiful giraffes do you see in the savannah?',\n", 411 | " 'black giraffes roam the savannah.',\n", 412 | " 'little giraffes roam the savannah.',\n", 413 | " 'the giraffes roam the savannah.',\n", 414 | " 'beautiful giraffes roam the park.',\n", 415 | " 'beautiful butterflies roam the savannah.',\n", 416 | " 'beautiful giraffes roam the land.',\n", 417 | " 'Beautiful giraffes roam the savannah.',\n", 418 | " 'large giraffes roam the savannah.',\n", 419 | " 'What are some impressive giraffes that roam the Savannah?',\n", 420 | " 'beautiful birds roam the savannah.',\n", 421 | " 'beautiful giraffes roam the streets.',\n", 422 | " 'beautiful giraffes roam the grounds.',\n", 423 | " 'beautiful animals roam the savannah.',\n", 424 | " 'In winter, in the middle of nowhere, a giraffe roams the Sabana Desert. What do they do?',\n", 425 | " 'beautiful creatures roam the savannah.',\n", 426 | " 'Beautiful giraffes roam the Savanna.',\n", 427 | " 'wild giraffes roam the savannah.',\n", 428 | " 'beautiful women roam the savannah.',\n", 429 | " 'beautiful giraffes roam the beach.']" 430 | ] 431 | }, 432 | "metadata": { 433 | "tags": [] 434 | }, 435 | "execution_count": 7 436 | } 437 | ] 438 | }, 439 | { 440 | "cell_type": "code", 441 | "metadata": { 442 | "colab": { 443 | "base_uri": "https://localhost:8080/" 444 | }, 445 | "id": "mzoLK-mq_H3e", 446 | "outputId": "eea8ad04-5cad-4094-b9d7-0dd601cec684" 447 | }, 448 | "source": [ 449 | "%%writefile dataset.csv\n", 450 | "Sue changed the flat tire., Label1\n", 451 | "The crew paved the entire stretch of highway., Label2\n", 452 | "The critic wrote a scathing review., Label1\n", 453 | "I will clean the house every Saturday., Label2 " 454 | ], 455 | "execution_count": 8, 456 | "outputs": [ 457 | { 458 | "output_type": "stream", 459 | "text": [ 460 | "Writing dataset.csv\n" 461 | ], 462 | "name": "stdout" 463 | } 464 | ] 465 | }, 466 | { 467 | "cell_type": "code", 468 | "metadata": { 469 | "colab": { 470 | "base_uri": "https://localhost:8080/" 471 | }, 472 | "id": "xzW11dvT_eNw", 473 | "outputId": "cf322632-0ef5-46df-a672-9d955dcc1e20" 474 | }, 475 | "source": [ 476 | "# Augment data in a csv file with labels\n", 477 | "augmented_dataset = textgenie.magic_lamp(\"dataset.csv\",\"paraphrase: \",n_paraphrase_predictions=15,n_mask_predictions=15,convert_to_active=True,label_column=\"Label\",column_names=[\"Text\",\"Label\"])" 478 | ], 479 | "execution_count": 3, 480 | "outputs": [ 481 | { 482 | "output_type": "stream", 483 | "text": [ 484 | "\r 0%| | 0/4 [00:00. In future versions this behavior may lead to duplicated eos tokens being added.\n", 487 | " f\"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated eos tokens being added.\"\n", 488 | "100%|██████████| 4/4 [00:30<00:00, 7.73s/it]" 489 | ], 490 | "name": "stderr" 491 | }, 492 | { 493 | "output_type": "stream", 494 | "text": [ 495 | "\n", 496 | "Completed writing output to /content/dataset_aug.csv.\n" 497 | ], 498 | "name": "stdout" 499 | }, 500 | { 501 | "output_type": "stream", 502 | "text": [ 503 | "\n" 504 | ], 505 | "name": "stderr" 506 | } 507 | ] 508 | }, 509 | { 510 | "cell_type": "code", 511 | "metadata": { 512 | "colab": { 513 | "base_uri": "https://localhost:8080/", 514 | "height": 402 515 | }, 516 | "id": "VdejwBeM_uWQ", 517 | "outputId": "23326c3a-a75d-4c9f-9d5d-09b58b37f252" 518 | }, 519 | "source": [ 520 | "augmented_dataset" 521 | ], 522 | "execution_count": 4, 523 | "outputs": [ 524 | { 525 | "output_type": "execute_result", 526 | "data": { 527 | "text/html": [ 528 | "
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