Initial commit

.gitignore

+.vscode/
+__pycache__/
+outputs/
+lightning_logs/
+mengzi-t5-base/
\ No newline at end of file

LICENSE

+MIT License
+
+Copyright (c) 2021 Shivanand Roy
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README copy.md

+<img align="center" src="data/st5.png" alt="simpleT5">
+
+<p align="center">
+<b>Quickly train T5/mT5/byT5/CodeT5 models in just 3 lines of code
+</b>
+</p>
+<p align="center">
+<a href="https://badge.fury.io/py/simplet5"><img src="https://badge.fury.io/py/simplet5.svg" alt="PyPI version" height="18"></a>
+
+<a href="https://badge.fury.io/py/simplet5">
+        <img alt="Stars" src="https://img.shields.io/github/stars/Shivanandroy/simpleT5?color=blue">
+    </a>
+<a href="https://pepy.tech/project/simplet5">
+        <img alt="Stats" src="https://static.pepy.tech/personalized-badge/simplet5?period=total&units=international_system&left_color=black&right_color=brightgreen&left_text=Downloads">
+    </a>
+<a href="https://opensource.org/licenses/MIT">
+        <img alt="License" src="https://img.shields.io/badge/License-MIT-yellow.svg">
+    </a>
+</p>
+
+
+**simpleT5** is built on top of PyTorch-lightning⚡️ and Transformers🤗 that lets you quickly train your T5 models.
+> T5 models can be used for several NLP tasks such as summarization, QA , QG , translation , text generation, and more. 
+
+
+Here's a link to [Medium article](https://snrspeaks.medium.com/simplet5-train-t5-models-in-just-3-lines-of-code-by-shivanand-roy-2021-354df5ae46ba) along with an [example colab notebook](https://colab.research.google.com/drive/1JZ8v9L0w0Ai3WbibTeuvYlytn0uHMP6O?usp=sharing) 
+
+## Install
+```python
+# It's advisable to create a new python environment and install simplet5
+pip install --upgrade simplet5
+```
+
+
+## Usage 
+**simpleT5** for summarization task [![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1JZ8v9L0w0Ai3WbibTeuvYlytn0uHMP6O?usp=sharing)
+```python
+# import
+from simplet5 import SimpleT5
+
+# instantiate
+model = SimpleT5()
+
+# load (supports t5, mt5, byT5 and CodeT5 models)
+model.from_pretrained("t5","t5-base")
+
+# train
+model.train(train_df=train_df, # pandas dataframe with 2 columns: source_text & target_text
+            eval_df=eval_df, # pandas dataframe with 2 columns: source_text & target_text
+            source_max_token_len = 512, 
+            target_max_token_len = 128,
+            batch_size = 8,
+            max_epochs = 5,
+            use_gpu = True,
+            outputdir = "outputs",
+            early_stopping_patience_epochs = 0,
+            precision = 32
+            )
+
+# load trained T5 model
+model.load_model("t5","path/to/trained/model/directory", use_gpu=False)
+
+# predict
+model.predict("input text for prediction")
+
+```
+## Articles
+- [Geek Culture: simpleT5 — Train T5 Models in Just 3 Lines of Code](https://medium.com/geekculture/simplet5-train-t5-models-in-just-3-lines-of-code-by-shivanand-roy-2021-354df5ae46ba)
+- [Abstractive Summarization with SimpleT5⚡️](https://snrspeaks.medium.com/abstractive-summarization-with-simplet5-%EF%B8%8F-344a78f73265)
+- [Training T5 model in just 3 lines of Code with ONNX Inference](https://medium.com/mlearning-ai/training-t5-model-in-just-3-lines-of-code-with-onnx-inference-ff5b6678c757)
+- [Kaggle: simpleT5⚡️ -  Generating one line summary of papers](https://www.kaggle.com/mathurinache/simplet5-generating-one-line-summary-of-papers)
+- [Youtube: Abstractive Summarization Demo with SimpleT5](https://www.youtube.com/watch?v=jgKj-7v2UYU)
+
+## Acknowledgements
+- [Transformers by HuggingFace 🤗](https://huggingface.co/transformers/)
+- [Pytorch Lightning ⚡️](https://www.pytorchlightning.ai/)
+- [Fastt5](https://github.com/Ki6an/fastT5)

README.md

+<img align="center" src="data/st5.png" alt="simpleT5">
+
+<p align="center">
+<b>Quickly train T5/mT5/byT5/CodeT5 models in just 3 lines of code
+</b>
+</p>
+<p align="center">
+<a href="https://badge.fury.io/py/simplet5"><img src="https://badge.fury.io/py/simplet5.svg" alt="PyPI version" height="18"></a>
+
+<a href="https://badge.fury.io/py/simplet5">
+        <img alt="Stars" src="https://img.shields.io/github/stars/Shivanandroy/simpleT5?color=blue">
+    </a>
+<a href="https://pepy.tech/project/simplet5">
+        <img alt="Stats" src="https://static.pepy.tech/personalized-badge/simplet5?period=total&units=international_system&left_color=black&right_color=brightgreen&left_text=Downloads">
+    </a>
+<a href="https://opensource.org/licenses/MIT">
+        <img alt="License" src="https://img.shields.io/badge/License-MIT-yellow.svg">
+    </a>
+</p>
+
+
+**simpleT5** is built on top of PyTorch-lightning⚡️ and Transformers🤗 that lets you quickly train your T5 models.
+> T5 models can be used for several NLP tasks such as summarization, QA , QG , translation , text generation, and more. 
+
+
+Here's a link to [Medium article](https://snrspeaks.medium.com/simplet5-train-t5-models-in-just-3-lines-of-code-by-shivanand-roy-2021-354df5ae46ba) along with an [example colab notebook](https://colab.research.google.com/drive/1JZ8v9L0w0Ai3WbibTeuvYlytn0uHMP6O?usp=sharing) 
+
+## Install
+```python
+# It's advisable to create a new python environment and install simplet5
+pip install --upgrade simplet5
+```
+
+
+## Usage 
+**simpleT5** for summarization task [![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1JZ8v9L0w0Ai3WbibTeuvYlytn0uHMP6O?usp=sharing)
+```python
+# import
+from simplet5 import SimpleT5
+
+# instantiate
+model = SimpleT5()
+
+# load (supports t5, mt5, byT5 and CodeT5 models)
+model.from_pretrained("t5","t5-base")
+
+# train
+model.train(train_df=train_df, # pandas dataframe with 2 columns: source_text & target_text
+            eval_df=eval_df, # pandas dataframe with 2 columns: source_text & target_text
+            source_max_token_len = 512, 
+            target_max_token_len = 128,
+            batch_size = 8,
+            max_epochs = 5,
+            use_gpu = True,
+            outputdir = "outputs",
+            early_stopping_patience_epochs = 0,
+            precision = 32
+            )
+
+# load trained T5 model
+model.load_model("t5","path/to/trained/model/directory", use_gpu=False)
+
+# predict
+model.predict("input text for prediction")
+
+```
+## Articles
+- [Geek Culture: simpleT5 — Train T5 Models in Just 3 Lines of Code](https://medium.com/geekculture/simplet5-train-t5-models-in-just-3-lines-of-code-by-shivanand-roy-2021-354df5ae46ba)
+- [Abstractive Summarization with SimpleT5⚡️](https://snrspeaks.medium.com/abstractive-summarization-with-simplet5-%EF%B8%8F-344a78f73265)
+- [Training T5 model in just 3 lines of Code with ONNX Inference](https://medium.com/mlearning-ai/training-t5-model-in-just-3-lines-of-code-with-onnx-inference-ff5b6678c757)
+- [Kaggle: simpleT5⚡️ -  Generating one line summary of papers](https://www.kaggle.com/mathurinache/simplet5-generating-one-line-summary-of-papers)
+- [Youtube: Abstractive Summarization Demo with SimpleT5](https://www.youtube.com/watch?v=jgKj-7v2UYU)
+
+## Acknowledgements
+- [Transformers by HuggingFace 🤗](https://huggingface.co/transformers/)
+- [Pytorch Lightning ⚡️](https://www.pytorchlightning.ai/)
+- [Fastt5](https://github.com/Ki6an/fastT5)

api.py

+from fastapi import FastAPI, Request
+import uvicorn, json, datetime, requests, os, time
+import torch
+import os
+
+from simplet5 import SimpleT5
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+PORT = 5555
+
+app = FastAPI()
+model = SimpleT5()
+
+model.tokenizer = T5Tokenizer.from_pretrained("./mengzi-t5-base")
+model.model = T5ForConditionalGeneration.from_pretrained("./outputs/simplet5-epoch-4-train-loss-0.1129-val-loss-0.2851")
+model.device = torch.device("cpu")
+
+@app.post("/disflu")
+async def disfluency_detection(request: Request):
+    json_post_raw = await request.json()
+    json_post = json.dumps(json_post_raw)
+    json_post_list = json.loads(json_post)
+    text = json_post_list.get('text')
+
+    print(text)
+    result = model.predict(text)
+
+    now = datetime.datetime.now()
+    now_time = now.strftime("%Y-%m-%d %H:%M:%S")
+
+    response = {
+        "result": result,
+        "status": 200,
+        "time": now_time
+    }
+
+    return response
+
+
+if __name__ == '__main__':
+    uvicorn.run(app, host='0.0.0.0', port=PORT, workers=1)
\ No newline at end of file

requirements.txt

+numpy
+pandas
+sentencepiece
+torch>=1.7.0,!=1.8.0
+transformers==4.16.2
+pytorch-lightning==1.5.10
+protobuf=3.20

setup.py

+import setuptools
+from os import path
+
+here = path.abspath(path.dirname(__file__))
+
+with open(path.join(here, "README.md"), encoding="utf-8") as f:
+    long_description = f.read()
+
+setuptools.setup(
+    name="simplet5",
+    version="0.1.4",
+    license="apache-2.0",
+    author="Shivanand Roy",
+    author_email="shivanandroy.official@gmail.com",
+    description="simpleT5 is built on top of PyTorch-lightning ⚡️ and Transformers 🤗 that lets you quickly train your T5 models.",
+    long_description=long_description,
+    long_description_content_type="text/markdown",
+    url="https://github.com/Shivanandroy/simpleT5",
+    project_urls={
+        "Repo": "https://github.com/Shivanandroy/simpleT5",
+        "Bug Tracker": "https://github.com/Shivanandroy/simpleT5/issues",
+    },
+    keywords=[
+        "T5",
+        "simpleT5",
+        "transformers",
+        "NLP",
+        "finetune",
+        "fine-tuning",
+        "pytorch",
+        "summarization",
+        "translation",
+        "training",
+        "classification",
+        "Q&A",
+        "inference",
+        "fast inference",
+    ],
+    packages=setuptools.find_packages(),
+    python_requires=">=3.5",
+    install_requires=[
+        "numpy",
+        "pandas",
+        "sentencepiece",
+        "torch>=1.7.0,!=1.8.0",  # excludes torch v1.8.0
+        "transformers==4.16.2",
+        "pytorch-lightning==1.5.10",
+    ],
+    classifiers=[
+        "Intended Audience :: Developers",
+        "Topic :: Scientific/Engineering :: Artificial Intelligence",
+        "License :: OSI Approved :: Apache Software License",
+        "Programming Language :: Python :: 3",
+        "Programming Language :: Python :: 3.5",
+        "Programming Language :: Python :: 3.6",
+        "Programming Language :: Python :: 3.7",
+        "Programming Language :: Python :: 3.8",
+    ],
+)

simplet5/__init__.py

+from .simplet5 import SimpleT5

test.py

+import torch
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+from simplet5 import SimpleT5
+
+model = SimpleT5()
+
+model_path = "./outputs/simplet5-epoch-4-train-loss-0.1129-val-loss-0.2851"
+model.tokenizer = T5Tokenizer.from_pretrained(model_path)
+model.model = T5ForConditionalGeneration.from_pretrained(model_path)
+model.device = torch.device("cpu")
+
+# model.load_model("t5","./outputs/simplet5-epoch-4-train-loss-0.1129-val-loss-0.2851", use_gpu=False)
+test1 = "你这是要准备干嘛呀？我，我再进一个人啊你这不就一个人吗？"
+test2 = "你这样不是会刺激吗？你这样。"
+
+result = model.predict("现在呢孩子大了已经读初中了，我呢自己呢这个也思反思了。")
+print(result)
+
+result1 = model.predict(test1)
+print(result1)
+
+result2 = model.predict(test2)
+print(result2)
\ No newline at end of file

test.sh

+curl -X POST "http://127.0.0.1:5555/disflu" \
+     -H 'Content-Type: application/json' \
+     -d '{"text": "现在呢孩子大了已经读初中了，我呢自己呢这个也思反思了。"}'
\ No newline at end of file