Initial commit

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)