REGEN-DISAMBIGUATION

Introduction

The REGEN-DISAMBIGUATION model by IBM is designed for text-to-text generation tasks, utilizing the T5 architecture. This model is implemented using PyTorch and is compatible with Hugging Face's Transformers library. It supports safe tensor formats and is designed for efficient inference.

Architecture

The model is based on the T5 (Text-to-Text Transfer Transformer) architecture, which is known for its versatility in handling various natural language processing tasks by converting them into a text-to-text format. It leverages PyTorch for model training and execution, ensuring flexibility and performance.

Training

The model is trained using text-to-text generation techniques to handle disambiguation tasks effectively. Specifics about the dataset and training parameters used are not detailed in the available documentation.

Guide: Running Locally

1. Clone the Repository:

   git clone https://huggingface.co/ibm/regen-disambiguation
   cd regen-disambiguation
   

2. Install Dependencies: Ensure you have the necessary libraries installed, such as transformers and torch.

   pip install transformers torch
   

3. Load the Model: Use the Transformers library to load and use the model.

   from transformers import T5ForConditionalGeneration, T5Tokenizer
   
   tokenizer = T5Tokenizer.from_pretrained('ibm/regen-disambiguation')
   model = T5ForConditionalGeneration.from_pretrained('ibm/regen-disambiguation')
   

4. Run Inference: Prepare your input text and use the model for generation.

   input_text = "Your input text here"
   input_ids = tokenizer.encode(input_text, return_tensors="pt")
   outputs = model.generate(input_ids)
   print(tokenizer.decode(outputs[0], skip_special_tokens=True))
   

For optimal performance, consider using a cloud GPU service such as AWS, Google Cloud, or Azure, which provide scalable resources suitable for model inference.

License

Details regarding the licensing of the REGEN-DISAMBIGUATION model are not explicitly provided in the available documentation. Users are advised to consult Hugging Face's model page or IBM's terms for more information.