├── tweegen ├── __init__.py ├── setup │ ├── env.sh │ └── model.sh ├── log.py ├── config.py ├── app.py ├── service.py └── core │ ├── twitter.py │ └── model.py ├── requirements ├── requirements_socle.txt ├── requirements_collector.txt ├── requirements_test.txt └── requirements_runtime.txt ├── .dockerignore ├── setup.py ├── Dockerfile ├── test ├── test_app.py └── test_service.py ├── README.md ├── .env.exemple ├── .github └── workflows │ └── python-app.yml ├── Makefile ├── .gitignore ├── LICENSE └── notebooks └── Train_TweeGen.ipynb /tweegen/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /requirements/requirements_socle.txt: -------------------------------------------------------------------------------- 1 | torch>=1.7.1 2 | transformers==4.2.2 3 | -------------------------------------------------------------------------------- /.dockerignore: -------------------------------------------------------------------------------- 1 | .git/ 2 | .gitignore 3 | .vscode 4 | data/ 5 | models/ 6 | notebooks/ 7 | -------------------------------------------------------------------------------- /requirements/requirements_collector.txt: -------------------------------------------------------------------------------- 1 | tqdm==4.55.0 2 | tweepy==3.10.0 3 | fire==0.3.1 4 | -------------------------------------------------------------------------------- /requirements/requirements_test.txt: -------------------------------------------------------------------------------- 1 | -r ./requirements_socle.txt 2 | 3 | fastapi==0.58.1 4 | pytest >=6.2.0,<7.0.0 5 | pytest-cov >=2.10.0,<3.0.0 6 | -------------------------------------------------------------------------------- /requirements/requirements_runtime.txt: -------------------------------------------------------------------------------- 1 | -r ./requirements_socle.txt 2 | -r ./requirements_collector.txt 3 | 4 | fastapi==0.58.1 5 | uvicorn==0.11.5 6 | loguru==0.5.1 7 | fire==0.3.1 8 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | from setuptools import setup 2 | 3 | setup( 4 | name="tweegen", 5 | version="0.1", 6 | packages=["tweegen"], 7 | url="", 8 | license="", 9 | author="Oscar DE FELICE", 10 | author_email="oscar.defelice@gmail.com", 11 | description="", 12 | ) 13 | -------------------------------------------------------------------------------- /Dockerfile: -------------------------------------------------------------------------------- 1 | FROM python:3.7.2 2 | 3 | LABEL maintainer="Oscar DE FELICE oscar.defelice@gmail.com" 4 | 5 | EXPOSE 80 6 | 7 | WORKDIR /var/task 8 | 9 | COPY requirements/ . 10 | 11 | RUN pip install --upgrade pip &&\ 12 | pip install -r requirements_runtime.txt 13 | 14 | COPY ./tweegen /tweegen 15 | 16 | RUN bash /tweegen/setup/model.sh 17 | 18 | CMD ["python", "/tweegen/app.py"] 19 | -------------------------------------------------------------------------------- /test/test_app.py: -------------------------------------------------------------------------------- 1 | from fastapi.testclient import TestClient 2 | 3 | from tweegen.app import app 4 | 5 | 6 | client = TestClient(app) 7 | 8 | 9 | def test_health(): 10 | response = client.get("/health").json() 11 | assert response["status_code"] == 200 12 | assert response["response"] == "ok" 13 | 14 | 15 | def test_predict(): 16 | response = client.get("/tweet") 17 | assert response.status_code == 200 18 | -------------------------------------------------------------------------------- /test/test_service.py: -------------------------------------------------------------------------------- 1 | from tweegen.service import load_model, _split, generate, upload 2 | from transformers import pipelines 3 | 4 | def test_load_model(): 5 | assert type(load_model()) == pipelines.text_generation.TextGenerationPipeline 6 | 7 | 8 | def test_split(): 9 | assert _split("a<|endoftext|>a") == ["a", "a"] 10 | 11 | 12 | def test_generate(): 13 | assert type(generate()) == list 14 | 15 | 16 | # def test_upload(): 17 | -------------------------------------------------------------------------------- /tweegen/setup/env.sh: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | 3 | ## Usage: 4 | ## . ./env.sh ; $COMMAND 5 | ## . ./env.sh ; echo ${ENV_VAR} 6 | 7 | unamestr=$(uname) 8 | if [ "$unamestr" = 'Linux' ]; then 9 | 10 | export $(grep -v '^#' .env | xargs -d '\n') 11 | 12 | elif [ "$unamestr" = 'Darwin' ]; then 13 | 14 | export $(grep -v '^#' .env | xargs -0) 15 | 16 | elif [ "$unamestr" = 'FreeBSD' ]; then 17 | 18 | export $(grep -v '^#' .env | xargs -0) 19 | 20 | fi 21 | -------------------------------------------------------------------------------- /tweegen/setup/model.sh: -------------------------------------------------------------------------------- 1 | wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1Zly3p3i7sc-ikg5CPg-GqTv19gzp29fo' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=1Zly3p3i7sc-ikg5CPg-GqTv19gzp29fo" -O models.zip && rm -rf /tmp/cookies.txt 2 | unzip models.zip -d models/ 3 | rm models.zip 4 | -------------------------------------------------------------------------------- /tweegen/log.py: -------------------------------------------------------------------------------- 1 | import time 2 | from loguru import logger 3 | from contextlib import contextmanager 4 | 5 | 6 | @contextmanager 7 | def log(desc): 8 | logger.opt(colors=True).debug(f"Service called: {desc}") 9 | start = time.time() 10 | try: 11 | yield 12 | except: 13 | logger.opt(colors=True).exception( 14 | f"Encountered error on service: {desc}" 15 | ) 16 | finally: 17 | end = time.time() 18 | logger.opt(colors=True).debug( 19 | f"Processing time for {desc}: {end-start} seconds" 20 | ) 21 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # tweet-generator 2 | A transformer model (GPT-2) based tweet generator. 3 | 4 | --- 5 | 6 | This repository is a tool to create a Twitter bot, able to generate tweets based on all tweets from a specific user. It handles all the following steps: 7 | 8 | - Tweets extraction 9 | - Language modeling 10 | - API deployement 11 | - Bot creation 12 | 13 | ### Prerequisites 14 | In order to use this project you should have credentials for a Twitter application created on their dev platform. Then you have to fill the missing fields in .env file (take .env.exemple as template). And finally make sure you are using a python environment. 15 | -------------------------------------------------------------------------------- /.env.exemple: -------------------------------------------------------------------------------- 1 | # This is an example of .env file 2 | # The real .env file contains secret information and should remain in .gitignore 3 | # Use: 4 | # cp .env.example .env 5 | 6 | # The following line will add "PORT" to the os.environ 7 | # os.environ["PORT"] == "8080" 8 | 9 | # API params 10 | PORT="80" 11 | LOG_LEVEL="info" 12 | 13 | # Twitter params 14 | CONSUMER_KEY="" 15 | CONSUMER_SECRET="" 16 | ACCESS_TOKEN="" 17 | ACCESS_TOKEN_SECRET="" 18 | 19 | # Model params 20 | PRETRAINED_MODEL="gpt2" 21 | MODEL_DIR="models/gpt2-tweetgen" 22 | STEPS="500" 23 | BATCH_SIZE="8" 24 | EPOCHS="3" 25 | TEMPERATURE="0.8" 26 | INPUT_TEXT="" 27 | RETURN_SEQUENCES="25" 28 | -------------------------------------------------------------------------------- /tweegen/config.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | PROJECT_NAME = "tweegen" 4 | VERSION = "0.1" 5 | PORT = int(os.getenv("PORT")) 6 | LOG_LEVEL = os.getenv("LOG_LEVEL") 7 | 8 | DATA_DIR = "data" 9 | DATA = "data/tweets.txt" 10 | SEP_WORD = "<|endoftext|>" 11 | 12 | CONSUMER_KEY = os.getenv("CONSUMER_KEY") 13 | CONSUMER_SECRET = os.getenv("CONSUMER_SECRET") 14 | ACCESS_TOKEN = os.getenv("ACCESS_TOKEN") 15 | ACCESS_TOKEN_SECRET = os.getenv("ACCESS_TOKEN_SECRET") 16 | 17 | PRETRAINED_MODEL = os.getenv("PRETRAINED_MODEL", "gpt2") 18 | MODEL_DIR = "models/gpt2-tweetgen" 19 | STEPS = int(os.getenv("STEPS", 500)) 20 | BATCH_SIZE = int(os.getenv("BATCH_SIZE", 8)) 21 | EPOCHS = float(os.getenv("EPOCHS", 3)) 22 | TEMPERATURE = float(os.getenv("TEMPERATURE", 0.8)) 23 | INPUT_TEXT = os.getenv("INPUT_TEXT", "") 24 | RETURN_SEQUENCES = int(os.getenv("RETURN_SEQUENCES", 25)) 25 | -------------------------------------------------------------------------------- /tweegen/app.py: -------------------------------------------------------------------------------- 1 | import uuid 2 | import uvicorn 3 | from fastapi import FastAPI 4 | from loguru import logger 5 | 6 | from tweegen.log import log 7 | from tweegen.config import PROJECT_NAME, VERSION, LOG_LEVEL, PORT 8 | from tweegen.service import load_model, generate, upload 9 | 10 | 11 | app = FastAPI(title=PROJECT_NAME, version=VERSION) 12 | 13 | 14 | @app.on_event("startup") 15 | def startup_event(): 16 | load_model() 17 | 18 | 19 | @app.get("/health", tags=["sanity"]) 20 | @log("Health check") 21 | def health(): 22 | return {"status_code": 200, "response": "ok"} 23 | 24 | 25 | @app.get( 26 | "/tweet", 27 | tags=["services"], 28 | summary="Generates bunch of tweets", 29 | ) 30 | @log("Tweet generated text") 31 | async def tweet(): 32 | """Generate tweets and upload them to environment user drafts""" 33 | with logger.contextualize(request_id=str(uuid.uuid4())): 34 | tweets = generate() 35 | upload(tweets) 36 | 37 | 38 | if __name__ == "__main__": 39 | uvicorn.run("app:app", host="0.0.0.0", port=PORT, log_level=LOG_LEVEL) 40 | -------------------------------------------------------------------------------- /tweegen/service.py: -------------------------------------------------------------------------------- 1 | import tweepy 2 | import random 3 | from transformers import pipeline 4 | 5 | import tweegen.config as cfg 6 | from tweegen.log import log 7 | 8 | 9 | 10 | @log("Model loading") 11 | def _load_model(): 12 | return pipeline('text-generation', 13 | model = cfg.MODEL_DIR, 14 | tokenizer = 'gpt2', 15 | config = {'max_length': 280}) 16 | 17 | 18 | def _split(text): 19 | return text.split(cfg.SEP_WORD) 20 | 21 | def _generate(): 22 | model = _load_model() 23 | tweet_lines = model(cfg.INPUT_TEXT, 24 | num_return_sequences = cfg.RETURN_SEQUENCES) 25 | 26 | tweets = [] 27 | for tweet_dict in tweet_lines: 28 | tweets.append(tweet_dict['generated_text']) 29 | 30 | return tweets 31 | 32 | 33 | @log("Tweets generating") 34 | def generate(): 35 | return _generate() 36 | 37 | 38 | @log("Tweets uploading") 39 | def upload(tweets): 40 | auth = tweepy.OAuthHandler(cfg.CONSUMER_KEY, cfg.CONSUMER_SECRET) 41 | auth.set_access_token(cfg.ACCESS_TOKEN, cfg.ACCESS_TOKEN_SECRET) 42 | api = tweepy.API(auth) 43 | api.update_status(random.choice(tweets)) 44 | -------------------------------------------------------------------------------- /.github/workflows/python-app.yml: -------------------------------------------------------------------------------- 1 | # This workflow will install Python dependencies, run tests and lint with a single version of Python 2 | # For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions 3 | 4 | name: Python application 5 | 6 | on: 7 | push: 8 | branches: [ main ] 9 | pull_request: 10 | branches: [ main ] 11 | 12 | jobs: 13 | build: 14 | 15 | runs-on: ubuntu-latest 16 | 17 | steps: 18 | - uses: actions/checkout@v2 19 | - name: Set up Python 3.9 20 | uses: actions/setup-python@v2 21 | with: 22 | python-version: 3.9 23 | - name: Install dependencies 24 | run: | 25 | python -m pip install --upgrade pip 26 | pip install flake8 pytest 27 | make init 28 | if [ -f requirements.txt ]; then pip install -r requirements.txt; fi 29 | - name: Lint with flake8 30 | run: | 31 | # stop the build if there are Python syntax errors or undefined names 32 | flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics 33 | # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide 34 | flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics 35 | - name: Test with pytest 36 | run: | 37 | pytest 38 | -------------------------------------------------------------------------------- /tweegen/core/twitter.py: -------------------------------------------------------------------------------- 1 | import fire 2 | import tweepy 3 | from tqdm import tqdm 4 | from pathlib import Path 5 | 6 | from tweegen.log import log 7 | from tweegen import config as cfg 8 | 9 | 10 | @log("Connect to the API") 11 | def _connect(): 12 | auth = tweepy.OAuthHandler(cfg.CONSUMER_KEY, cfg.CONSUMER_SECRET) 13 | auth.set_access_token(cfg.ACCESS_TOKEN, cfg.ACCESS_TOKEN_SECRET) 14 | Path(cfg.DATA_DIR).mkdir(exist_ok=True) 15 | return tweepy.API(auth) 16 | 17 | 18 | def _clean(txt): 19 | return " ".join( 20 | [ 21 | w if not w.endswith("...") else w[:-3] 22 | for w in txt.split() 23 | if not w.startswith("http") and not w.startswith("@") 24 | ] 25 | ) 26 | 27 | 28 | @log("Download tweets") 29 | def _download_tweets(api, name): 30 | path = Path(cfg.DATA_DIR) / name 31 | path.mkdir(exist_ok=True) 32 | user = api.get_user(name) 33 | for tweet in tqdm( 34 | tweepy.Cursor( 35 | api.user_timeline, 36 | screen_name=name, 37 | tweet_mode="extended", 38 | include_rts=False, 39 | wait_on_rate_limit=True, 40 | ).items(), 41 | total=user.statuses_count, 42 | ): 43 | if tweet: 44 | with open(path / "tweets.txt", "a") as f: 45 | f.write(_clean(tweet.full_text) + cfg.SEP_WORD) 46 | 47 | 48 | def download(name="BernieSanders"): 49 | api = _connect() 50 | _download_tweets(api, name) 51 | 52 | 53 | if __name__ == "__main__": 54 | fire.Fire() 55 | -------------------------------------------------------------------------------- /Makefile: -------------------------------------------------------------------------------- 1 | SLUG := tweegen 2 | DOCKER_IMAGE_LOCAL := "${SLUG}-local" 3 | DOCKER_IMAGE_PROD := ${SLUG} 4 | 5 | ENV_FILE := ".env" 6 | 7 | args = `arg="$(filter-out $@,$(MAKECMDGOALS))" && echo $${arg:-${1}}` 8 | 9 | clean: ## remove all build, test, coverage and Python artifacts 10 | @rm -fr build/ dist/ .eggs/ public/ 11 | @find . -name '*.egg-info' -exec rm -fr {} + 12 | @find . -name '*.egg' -exec rm -f {} + 13 | 14 | @find . -name '*.pyc' -exec rm -f {} + 15 | @find . -name '*.pyo' -exec rm -f {} + 16 | @find . -name '*~' -exec rm -f {} + 17 | @find . -name '__pycache__' -exec rm -fr {} + 18 | 19 | @rm -fr .coverage cov-report .pytest_cache 20 | 21 | 22 | test: ## run tests and coverage quickly with the default Python 23 | pytest 24 | 25 | 26 | install-dev-socle: ## install the package for developement 27 | make clean 28 | pip install -r requirements/requirements_socle.txt 29 | 30 | 31 | install-dev-collector: ## install the package for developement 32 | make clean 33 | pip3 install -r requirements/requirements_collector.txt 34 | 35 | 36 | install-dev: ## install the package for developement 37 | make install-dev-socle 38 | make install-dev-collector 39 | 40 | 41 | install: ## install the package to the active Python's site-packages 42 | make clean 43 | pip3 install -r requirements/requirements_runtime.txt 44 | pip3 install -r requirements/requirements_test.txt 45 | 46 | 47 | setup-env: ## setup environment variable 48 | . tweegen/setup/env.sh 49 | python3 setup.py install 50 | 51 | 52 | download-model: ## download model repository for finetuning 53 | bash tweegen/setup/model.sh 54 | 55 | 56 | init: ## setup the dev enviroment 57 | @echo "Installing requirements" 58 | make install 59 | @echo "Setting up environment" 60 | make setup-env 61 | @echo "Downloading pretrained models" 62 | make download-model 63 | @echo "Initialisation finished" 64 | 65 | download: 66 | python3 tweegen/core/twitter.py download $(call args,BernieSanders) 67 | 68 | train: 69 | python3 tweegen/core/model.py train $(call args,BernieSanders) 70 | 71 | generate: 72 | python3 tweegen/core/model.py generate $(call args,BernieSanders) 73 | 74 | 75 | docker-build: ## [DEV ] build docker image without downloading the model 76 | @echo "Building image ${DOCKER_IMAGE_LOCAL}" 77 | docker build -t ${DOCKER_IMAGE_LOCAL} . 78 | 79 | 80 | docker-exec: ## [DEV ] run cmd inside dev docker 81 | docker run --rm \ 82 | --name ${DOCKER_IMAGE_LOCAL} \ 83 | --env-file= ${ENV_FILE} \ 84 | --it ${DOCKER_IMAGE_LOCAL} 85 | 86 | %: 87 | @: 88 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$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 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 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 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | 131 | # Data 132 | data/ 133 | models/ 134 | 135 | # OS files 136 | .DS_Store 137 | -------------------------------------------------------------------------------- /tweegen/core/model.py: -------------------------------------------------------------------------------- 1 | import fire 2 | from pathlib import Path 3 | from transformers import pipeline 4 | from transformers import AutoTokenizer 5 | from transformers import Trainer, TrainingArguments, AutoModelWithLMHead 6 | from transformers import TextDataset, DataCollatorForLanguageModeling 7 | 8 | from tweegen import log 9 | from tweegen import config as cfg 10 | 11 | tokenizer = AutoTokenizer.from_pretrained(cfg.PRETRAINED_MODEL) 12 | 13 | def _load_dataset(): 14 | 15 | train_dataset = TextDataset( 16 | tokenizer=tokenizer, 17 | file_path=cfg.DATA, 18 | block_size=128) 19 | 20 | data_collator = DataCollatorForLanguageModeling( 21 | tokenizer=tokenizer, mlm=False, 22 | ) 23 | return train_dataset, data_collator 24 | 25 | def _get_trainer(): 26 | model = AutoModelWithLMHead.from_pretrained(cfg.PRETRAINED_MODEL) 27 | 28 | training_args = TrainingArguments( 29 | output_dir= cfg.MODEL_DIR, #The output directory 30 | overwrite_output_dir=True, #overwrite the content of the output directory 31 | num_train_epochs=cfg.EPOCHS, # number of training epochs 32 | per_device_train_batch_size=cfg.BATCH_SIZE, # batch size for training 33 | eval_steps = cfg.STEPS, # Number of update steps between two evaluations. 34 | save_steps=cfg.STEPS, # after # steps model is saved 35 | warmup_steps=cfg.STEPS,# number of warmup steps for learning rate scheduler 36 | ) 37 | 38 | train_dataset, data_collator = _load_dataset() 39 | 40 | return Trainer( 41 | model=model, 42 | args=training_args, 43 | data_collator=data_collator, 44 | train_dataset=train_dataset, 45 | ) 46 | 47 | @log("Model Training") 48 | def train(name): 49 | path = Path(cfg.DATA_DIR) / name 50 | trainer = _get_trainer() 51 | trainer.train() 52 | trainer.save_model() 53 | 54 | @log("Fake Tweets generating") 55 | def _generate(): 56 | model = pipeline('text-generation', 57 | model = cfg.MODEL_DIR, 58 | tokenizer = 'gpt2', 59 | config = {'max_length': 280}) 60 | text_lines = model( 61 | cfg.INPUT_TEXT, 62 | num_return_sequences = cfg.RETURN_SEQUENCES 63 | ) 64 | 65 | return text_lines 66 | 67 | 68 | def generate(): 69 | text_lines = _generate() 70 | for text_dict in text_lines: 71 | print(text_dict['generated_text']) 72 | print('\n') 73 | 74 | 75 | if __name__ == "__main__": 76 | fire.Fire() 77 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. 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If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /notebooks/Train_TweeGen.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "name": "Train-TweeGen", 7 | "provenance": [], 8 | "collapsed_sections": [], 9 | "toc_visible": true 10 | }, 11 | "kernelspec": { 12 | "name": "python3", 13 | "display_name": "Python 3" 14 | }, 15 | "accelerator": "GPU", 16 | "widgets": { 17 | "application/vnd.jupyter.widget-state+json": { 18 | "169795ac5899428abc59e4bbd1c31fb2": { 19 | "model_module": "@jupyter-widgets/controls", 20 | "model_name": "HBoxModel", 21 | "state": { 22 | "_view_name": "HBoxView", 23 | "_dom_classes": [], 24 | "_model_name": "HBoxModel", 25 | "_view_module": "@jupyter-widgets/controls", 26 | "_model_module_version": "1.5.0", 27 | "_view_count": null, 28 | "_view_module_version": "1.5.0", 29 | 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1200 | "_view_module": "@jupyter-widgets/base", 1201 | "overflow": null, 1202 | "_model_module_version": "1.2.0", 1203 | "_view_count": null, 1204 | "flex_flow": null, 1205 | "width": null, 1206 | "min_width": null, 1207 | "border": null, 1208 | "align_items": null, 1209 | "bottom": null, 1210 | "_model_module": "@jupyter-widgets/base", 1211 | "top": null, 1212 | "grid_column": null, 1213 | "overflow_y": null, 1214 | "overflow_x": null, 1215 | "grid_auto_flow": null, 1216 | "grid_area": null, 1217 | "grid_template_columns": null, 1218 | "flex": null, 1219 | "_model_name": "LayoutModel", 1220 | "justify_items": null, 1221 | "grid_row": null, 1222 | "max_height": null, 1223 | "align_content": null, 1224 | "visibility": null, 1225 | "align_self": null, 1226 | "height": null, 1227 | "min_height": null, 1228 | "padding": null, 1229 | "grid_auto_rows": null, 1230 | "grid_gap": null, 1231 | "max_width": null, 1232 | "order": null, 1233 | "_view_module_version": "1.2.0", 1234 | "grid_template_areas": null, 1235 | "object_position": null, 1236 | "object_fit": null, 1237 | "grid_auto_columns": null, 1238 | "margin": null, 1239 | "display": null, 1240 | "left": null 1241 | } 1242 | } 1243 | } 1244 | } 1245 | }, 1246 | "cells": [ 1247 | { 1248 | "cell_type": "markdown", 1249 | "metadata": { 1250 | "id": "nVNIlGjIxffD" 1251 | }, 1252 | "source": [ 1253 | "# Training a tweet generator model\n" 1254 | ] 1255 | }, 1256 | { 1257 | "cell_type": "code", 1258 | "metadata": { 1259 | "colab": { 1260 | "base_uri": "https://localhost:8080/", 1261 | "height": 213, 1262 | "referenced_widgets": [ 1263 | "169795ac5899428abc59e4bbd1c31fb2", 1264 | "8109b891da804ea1812300be930293bc", 1265 | "ae468860eaa541b98a266b42d7cc25a0", 1266 | "24f54adb9ede4ee1bb55020f03b1d0aa", 1267 | "f2c0610c2e5348c199d4665f162e8067", 1268 | "8a044d24709f4a75adab056f7f1b1a19", 1269 | "60dd9c14ef364210933e892d1d6f3070", 1270 | "5fa8a3d8c5cb4f45aa1aa21b11c0200e", 1271 | "16f2d3c06a4b4d1f8d290ba9307359ae", 1272 | "f568a5a969004c0785c253c0f1c007e0", 1273 | "199ca4ca35024e579297d062f581fceb", 1274 | "a85541beb1574a3cbeffe1e2a7f68e9a", 1275 | "388aa3a3b61640798d51ef5873b4527d", 1276 | "8f45b4db30364904aa881d0eb6eb3664", 1277 | "997d9df0b1b44cbda161dd6aede6033b", 1278 | "bbd3f600363443e380d325073ffe2c94", 1279 | "6185dd576a7c4378bdfaca5ac188ad04", 1280 | "17cca8bf255046e383014412592177f3", 1281 | "d6144f472ade45068b7caa255e568391", 1282 | "ba19d902c23845b49e7611ef858e162d", 1283 | "bf7c641b24764c33a09afac8ad31637e", 1284 | "971861dbfb5f48ca94776f53eeb21d64", 1285 | "651bb42eb283408a85fdc2109ade9387", 1286 | "7558ed69090f4c40b22e6a0ca8268c43", 1287 | "5161accdbffe44768b5895b6d53daf48", 1288 | "4291cb0e3c3942d387b83a18e2fba993", 1289 | "bc37533e085a4c37aa3991bd6d216b02", 1290 | "66666e44a2d0421da1ce7ddb9c20b61e", 1291 | "79cff82ab6db4491a6c82157c3acbe61", 1292 | "bc096c5ededb48128858f8c4c6765997", 1293 | "85ea67060f994a0886fb0eb3c0a996f4", 1294 | "750f585dd68b421cb18e299fcbde3700" 1295 | ] 1296 | }, 1297 | "id": "p8xG94Adz1e_", 1298 | "outputId": "8ec630d9-4e3d-45d0-ff33-2b8f7dac5afd" 1299 | }, 1300 | "source": [ 1301 | "from transformers import AutoTokenizer\n", 1302 | "\n", 1303 | "tokenizer = AutoTokenizer.from_pretrained(\"gpt2\")" 1304 | ], 1305 | "execution_count": 2, 1306 | "outputs": [ 1307 | { 1308 | "output_type": "display_data", 1309 | "data": { 1310 | "application/vnd.jupyter.widget-view+json": { 1311 | "model_id": "169795ac5899428abc59e4bbd1c31fb2", 1312 | "version_minor": 0, 1313 | "version_major": 2 1314 | }, 1315 | "text/plain": [ 1316 | "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=665.0, style=ProgressStyle(description_…" 1317 | ] 1318 | }, 1319 | "metadata": { 1320 | "tags": [] 1321 | } 1322 | }, 1323 | { 1324 | "output_type": "stream", 1325 | "text": [ 1326 | "\n" 1327 | ], 1328 | "name": "stdout" 1329 | }, 1330 | { 1331 | "output_type": "display_data", 1332 | "data": { 1333 | "application/vnd.jupyter.widget-view+json": { 1334 | "model_id": "16f2d3c06a4b4d1f8d290ba9307359ae", 1335 | "version_minor": 0, 1336 | "version_major": 2 1337 | }, 1338 | "text/plain": [ 1339 | "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1042301.0, style=ProgressStyle(descript…" 1340 | ] 1341 | }, 1342 | "metadata": { 1343 | "tags": [] 1344 | } 1345 | }, 1346 | { 1347 | "output_type": "stream", 1348 | "text": [ 1349 | "\n" 1350 | ], 1351 | "name": "stdout" 1352 | }, 1353 | { 1354 | "output_type": "display_data", 1355 | "data": { 1356 | "application/vnd.jupyter.widget-view+json": { 1357 | "model_id": "6185dd576a7c4378bdfaca5ac188ad04", 1358 | "version_minor": 0, 1359 | "version_major": 2 1360 | }, 1361 | "text/plain": [ 1362 | "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=456318.0, style=ProgressStyle(descripti…" 1363 | ] 1364 | }, 1365 | "metadata": { 1366 | "tags": [] 1367 | } 1368 | }, 1369 | { 1370 | "output_type": "stream", 1371 | "text": [ 1372 | "\n" 1373 | ], 1374 | "name": "stdout" 1375 | }, 1376 | { 1377 | "output_type": "display_data", 1378 | "data": { 1379 | "application/vnd.jupyter.widget-view+json": { 1380 | "model_id": "5161accdbffe44768b5895b6d53daf48", 1381 | "version_minor": 0, 1382 | "version_major": 2 1383 | }, 1384 | "text/plain": [ 1385 | "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1355256.0, style=ProgressStyle(descript…" 1386 | ] 1387 | }, 1388 | "metadata": { 1389 | "tags": [] 1390 | } 1391 | }, 1392 | { 1393 | "output_type": "stream", 1394 | "text": [ 1395 | "\n" 1396 | ], 1397 | "name": "stdout" 1398 | } 1399 | ] 1400 | }, 1401 | { 1402 | "cell_type": "code", 1403 | "metadata": { 1404 | "colab": { 1405 | "base_uri": "https://localhost:8080/" 1406 | }, 1407 | "id": "lrl8jp1S0TL3", 1408 | "outputId": "47a696ee-e58b-4d64-bca1-021ac6f59892" 1409 | }, 1410 | "source": [ 1411 | "from transformers import TextDataset,DataCollatorForLanguageModeling\n", 1412 | "\n", 1413 | "def load_dataset(train_path,tokenizer):\n", 1414 | " train_dataset = TextDataset(\n", 1415 | " tokenizer=tokenizer,\n", 1416 | " file_path=train_path,\n", 1417 | " block_size=128)\n", 1418 | " \n", 1419 | "# test_dataset = TextDataset(\n", 1420 | "# tokenizer=tokenizer,\n", 1421 | "# file_path=test_path,\n", 1422 | "# block_size=128) \n", 1423 | " \n", 1424 | " data_collator = DataCollatorForLanguageModeling(\n", 1425 | " tokenizer=tokenizer, mlm=False,\n", 1426 | " )\n", 1427 | " return train_dataset ,data_collator\n", 1428 | "\n", 1429 | "train_dataset ,data_collator = load_dataset('tweets_train.txt', tokenizer)" 1430 | ], 1431 | "execution_count": 3, 1432 | "outputs": [ 1433 | { 1434 | "output_type": "stream", 1435 | "text": [ 1436 | "/usr/local/lib/python3.6/dist-packages/transformers/data/datasets/language_modeling.py:58: FutureWarning: This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets library. You can have a look at this example script for pointers: https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm.py\n", 1437 | " FutureWarning,\n", 1438 | "Token indices sequence length is longer than the specified maximum sequence length for this model (113470 > 1024). Running this sequence through the model will result in indexing errors\n" 1439 | ], 1440 | "name": "stderr" 1441 | } 1442 | ] 1443 | }, 1444 | { 1445 | "cell_type": "code", 1446 | "metadata": { 1447 | "colab": { 1448 | "base_uri": "https://localhost:8080/", 1449 | "height": 120, 1450 | "referenced_widgets": [ 1451 | "322c8fb0b1bf40b9b2870e7fe1475361", 1452 | "803635b8dc1645b5bc38ff6f6c4a6ccf", 1453 | "819cfb49894f454491e16026ce133499", 1454 | "84a3bea1d5614030b1f2397ee493214e", 1455 | "08d10b0c763f4ac0976d90ccbb839b85", 1456 | "9e54ec935dc54fb6abfb62bf8a373af5", 1457 | "0655410dd4f3474cbe8fe2d082c1e4eb", 1458 | "f4705878eca6477d8eef19017330bfb5" 1459 | ] 1460 | }, 1461 | "id": "eOKPBgJ30kJ1", 1462 | "outputId": "30150e33-c726-4cd6-e2b8-4ea7405af115" 1463 | }, 1464 | "source": [ 1465 | "from transformers import Trainer, TrainingArguments,AutoModelWithLMHead\n", 1466 | "\n", 1467 | "model = AutoModelWithLMHead.from_pretrained(\"gpt2\")\n", 1468 | "\n", 1469 | "training_args = TrainingArguments(\n", 1470 | " output_dir=\"./gpt2-tweetgen\", #The output directory\n", 1471 | " overwrite_output_dir=True, #overwrite the content of the output directory\n", 1472 | " num_train_epochs=3, # number of training epochs\n", 1473 | " per_device_train_batch_size=32, # batch size for training\n", 1474 | " per_device_eval_batch_size=64, # batch size for evaluation\n", 1475 | " eval_steps = 400, # Number of update steps between two evaluations.\n", 1476 | " save_steps=800, # after # steps model is saved \n", 1477 | " warmup_steps=500,# number of warmup steps for learning rate scheduler\n", 1478 | " )\n", 1479 | "\n", 1480 | "\n", 1481 | "trainer = Trainer(\n", 1482 | " model=model,\n", 1483 | " args=training_args,\n", 1484 | " data_collator=data_collator,\n", 1485 | " train_dataset=train_dataset,\n", 1486 | ")" 1487 | ], 1488 | "execution_count": 4, 1489 | "outputs": [ 1490 | { 1491 | "output_type": "stream", 1492 | "text": [ 1493 | "/usr/local/lib/python3.6/dist-packages/transformers/models/auto/modeling_auto.py:925: FutureWarning: The class `AutoModelWithLMHead` is deprecated and will be removed in a future version. Please use `AutoModelForCausalLM` for causal language models, `AutoModelForMaskedLM` for masked language models and `AutoModelForSeq2SeqLM` for encoder-decoder models.\n", 1494 | " FutureWarning,\n" 1495 | ], 1496 | "name": "stderr" 1497 | }, 1498 | { 1499 | "output_type": "display_data", 1500 | "data": { 1501 | "application/vnd.jupyter.widget-view+json": { 1502 | "model_id": "322c8fb0b1bf40b9b2870e7fe1475361", 1503 | "version_minor": 0, 1504 | "version_major": 2 1505 | }, 1506 | "text/plain": [ 1507 | "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=548118077.0, style=ProgressStyle(descri…" 1508 | ] 1509 | }, 1510 | "metadata": { 1511 | "tags": [] 1512 | } 1513 | }, 1514 | { 1515 | "output_type": "stream", 1516 | "text": [ 1517 | "\n" 1518 | ], 1519 | "name": "stdout" 1520 | } 1521 | ] 1522 | }, 1523 | { 1524 | "cell_type": "code", 1525 | "metadata": { 1526 | "colab": { 1527 | "base_uri": "https://localhost:8080/", 1528 | "height": 112 1529 | }, 1530 | "id": "MEno7Hgr1Anw", 1531 | "outputId": "5b193783-397d-4fee-df56-5a4d6d36dac5" 1532 | }, 1533 | "source": [ 1534 | "trainer.train()" 1535 | ], 1536 | "execution_count": 5, 1537 | "outputs": [ 1538 | { 1539 | "output_type": "display_data", 1540 | "data": { 1541 | "text/html": [ 1542 | "\n", 1543 | "
\n", 1544 | " \n", 1553 | " \n", 1554 | " \n", 1555 | " [84/84 01:36, Epoch 3/3]\n", 1556 | "
\n", 1557 | " \n", 1558 | " \n", 1559 | " \n", 1560 | " \n", 1561 | " \n", 1562 | " \n", 1563 | " \n", 1564 | " \n", 1565 | " \n", 1566 | "
StepTraining Loss

" 1567 | ], 1568 | "text/plain": [ 1569 | "" 1570 | ] 1571 | }, 1572 | "metadata": { 1573 | "tags": [] 1574 | } 1575 | }, 1576 | { 1577 | "output_type": "execute_result", 1578 | "data": { 1579 | "text/plain": [ 1580 | "TrainOutput(global_step=84, training_loss=3.549079168410528, metrics={'train_runtime': 97.5001, 'train_samples_per_second': 0.862, 'total_flos': 254024455421952, 'epoch': 3.0})" 1581 | ] 1582 | }, 1583 | "metadata": { 1584 | "tags": [] 1585 | }, 1586 | "execution_count": 5 1587 | } 1588 | ] 1589 | }, 1590 | { 1591 | "cell_type": "code", 1592 | "metadata": { 1593 | "id": "TOTbP42X1q9b" 1594 | }, 1595 | "source": [ 1596 | "trainer.save_model()" 1597 | ], 1598 | "execution_count": 6, 1599 | "outputs": [] 1600 | }, 1601 | { 1602 | "cell_type": "code", 1603 | "metadata": { 1604 | "id": "jGRP85uT3psQ" 1605 | }, 1606 | "source": [ 1607 | "from transformers import pipeline\n", 1608 | "\n", 1609 | "generator = pipeline('text-generation',model='./gpt2-tweetgen', tokenizer='gpt2',config={'max_length':280})\n" 1610 | ], 1611 | "execution_count": 10, 1612 | "outputs": [] 1613 | }, 1614 | { 1615 | "cell_type": "code", 1616 | "metadata": { 1617 | "colab": { 1618 | "base_uri": "https://localhost:8080/" 1619 | }, 1620 | "id": "DQmAuGy833Mr", 1621 | "outputId": "e9eea8c7-5e9a-4d25-bf46-9485e56a30bb" 1622 | }, 1623 | "source": [ 1624 | "generator('America')" 1625 | ], 1626 | "execution_count": 12, 1627 | "outputs": [ 1628 | { 1629 | "output_type": "stream", 1630 | "text": [ 1631 | "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n" 1632 | ], 1633 | "name": "stderr" 1634 | }, 1635 | { 1636 | "output_type": "execute_result", 1637 | "data": { 1638 | "text/plain": [ 1639 | "[{'generated_text': \"America. (Don't forget about Trump's tweet!)\"}]" 1640 | ] 1641 | }, 1642 | "metadata": { 1643 | "tags": [] 1644 | }, 1645 | "execution_count": 12 1646 | } 1647 | ] 1648 | }, 1649 | { 1650 | "cell_type": "code", 1651 | "metadata": { 1652 | "colab": { 1653 | "base_uri": "https://localhost:8080/" 1654 | }, 1655 | "id": "-rWLndu94iDO", 1656 | "outputId": "5156b8b9-b079-43a8-f8d7-d917b2d0e6fc" 1657 | }, 1658 | "source": [ 1659 | "generator('Me')" 1660 | ], 1661 | "execution_count": 14, 1662 | "outputs": [ 1663 | { 1664 | "output_type": "stream", 1665 | "text": [ 1666 | "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n" 1667 | ], 1668 | "name": "stderr" 1669 | }, 1670 | { 1671 | "output_type": "execute_result", 1672 | "data": { 1673 | "text/plain": [ 1674 | "[{'generated_text': \"Me on. We are all very, very sad to hear about what has been done to this family after so many years. We want to thank those who were here to help us, like our president's team, our state officials, to fight for\"}]" 1675 | ] 1676 | }, 1677 | "metadata": { 1678 | "tags": [] 1679 | }, 1680 | "execution_count": 14 1681 | } 1682 | ] 1683 | }, 1684 | { 1685 | "cell_type": "code", 1686 | "metadata": { 1687 | "colab": { 1688 | "base_uri": "https://localhost:8080/" 1689 | }, 1690 | "id": "QxHE-E2A4pPi", 1691 | "outputId": "01ae65d9-2f92-4809-ac83-2c0ffc3830fa" 1692 | }, 1693 | "source": [ 1694 | "generator('Bernie')" 1695 | ], 1696 | "execution_count": 15, 1697 | "outputs": [ 1698 | { 1699 | "output_type": "stream", 1700 | "text": [ 1701 | "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n" 1702 | ], 1703 | "name": "stderr" 1704 | }, 1705 | { 1706 | "output_type": "execute_result", 1707 | "data": { 1708 | "text/plain": [ 1709 | "[{'generated_text': 'Bernie and Senator Sanders today joined us today in calling for Donald Rumsfeld to be impeached for lying to Vice President Joe Biden about his support for war in Iraq.\\n\\nLet me come to you now and discuss what is at stake for the'}]" 1710 | ] 1711 | }, 1712 | "metadata": { 1713 | "tags": [] 1714 | }, 1715 | "execution_count": 15 1716 | } 1717 | ] 1718 | }, 1719 | { 1720 | "cell_type": "code", 1721 | "metadata": { 1722 | "colab": { 1723 | "base_uri": "https://localhost:8080/" 1724 | }, 1725 | "id": "NFsw2Qsq4wRT", 1726 | "outputId": "795a4c14-4e3f-44d9-cc7b-09fcef9f74fc" 1727 | }, 1728 | "source": [ 1729 | "generator('')" 1730 | ], 1731 | "execution_count": 16, 1732 | "outputs": [ 1733 | { 1734 | "output_type": "stream", 1735 | "text": [ 1736 | "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n" 1737 | ], 1738 | "name": "stderr" 1739 | }, 1740 | { 1741 | "output_type": "execute_result", 1742 | "data": { 1743 | "text/plain": [ 1744 | "[{'generated_text': 'The next wave, as Donald Trump has vowed, will target American workers. A few small groups are trying for change: young workers, minorities and people of color.\\n\\nThe American Dream deserves a president who will make fair and free trade a'}]" 1745 | ] 1746 | }, 1747 | "metadata": { 1748 | "tags": [] 1749 | }, 1750 | "execution_count": 16 1751 | } 1752 | ] 1753 | }, 1754 | { 1755 | "cell_type": "code", 1756 | "metadata": { 1757 | "id": "zQJSaWRU41Ds" 1758 | }, 1759 | "source": [ 1760 | "" 1761 | ], 1762 | "execution_count": null, 1763 | "outputs": [] 1764 | } 1765 | ] 1766 | } --------------------------------------------------------------------------------