simplet5 0.1.1

simpleT5 is built on top of PyTorch-lightning ⚡️ and Transformers 🤗 that lets you quickly train your T5 models.

Quickly train T5 models in just 3 lines of code with ONNX inference

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 along with an example colab notebook

Install

pip install --upgrade simplet5

Usage

simpleT5 for summarization task

# import
from simplet5 import SimpleT5

# instantiate
model = SimpleT5()

# load (supports t5, mt5, byt5)
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")

# need faster inference on CPU, get ONNX support
model.convert_and_load_onnx_model("path/to/T5 model/directory")
model.onnx_predict("input text for prediction")

Articles

• Geek Culture: simpleT5 — Train T5 Models in Just 3 Lines of Code
• Abstractive Summarization with SimpleT5⚡️
• Training T5 model in just 3 lines of Code with ONNX Inference
• Kaggle: simpleT5⚡️ - Generating one line summary of papers
• Youtube: Abstractive Summarization Demo with SimpleT5

Acknowledgements

• Transformers by HuggingFace 🤗
• Pytorch Lightning ⚡️
• Fastt5