mtl data to text

Introduction

The MTL-data-to-text model, designed for data-to-text generation tasks, is a variant of the MVP model. It is specifically tailored for converting structured data to natural language text, useful in applications like KG-to-text, table-to-text, and MR-to-text generation.

Architecture

MTL-data-to-text employs a standard Transformer encoder-decoder architecture. It has been pre-trained on a diverse set of labeled data-to-text datasets, enhancing its capability to generate coherent and contextually relevant text from structured data inputs.

Training

The model is pre-trained in a supervised manner, utilizing a mixture of labeled datasets that focus on data-to-text transformation. This pre-training strategy is part of the overarching MVP framework, which aims to improve natural language generation across multiple tasks.

Guide: Running Locally

To run the MTL-data-to-text model locally, follow these steps:

1. Install the Transformers Library: Ensure that you have the transformers library installed.

   pip install transformers
   

2. Load the Model and Tokenizer:

   from transformers import MvpTokenizer, MvpForConditionalGeneration
   
   tokenizer = MvpTokenizer.from_pretrained("RUCAIBox/mvp")
   model = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mtl-data-to-text")
   

3. Prepare Input and Generate Text:

   inputs = tokenizer(
       "Describe the following data: Iron Man | instance of | Superhero [SEP] Stan Lee | creator | Iron Man",
       return_tensors="pt",
   )
   generated_ids = model.generate(**inputs)
   output = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
   print(output)
   

4. Cloud GPUs: For larger datasets or faster processing, consider using cloud services like AWS, Google Cloud, or Azure to access high-performance GPUs.

License

This model is licensed under the Apache 2.0 License, allowing for both personal and commercial use, modification, and distribution.