byt5 large

Introduction

ByT5-Large is a tokenizer-free variant of Google's T5 model designed for text-to-text generation tasks. It is part of the ByT5 family and processes text at the byte level, allowing it to handle multilingual and noisy text data effectively. ByT5-Large is pre-trained on the multilingual C4 dataset (mC4) and requires fine-tuning for specific downstream tasks.

Architecture

ByT5 follows the Transformer architecture, modified to process sequences of UTF-8 bytes instead of tokens. This design choice enables ByT5 to bypass the need for a tokenizer, making it robust to text noise and capable of handling multiple languages. The model architecture is based on MT5 and is optimized to handle longer byte sequences efficiently.

Training

ByT5 was pre-trained using the mC4 dataset, with a focus on processing noisy text without supervised training. The pre-training involves a span-mask of 20 UTF-8 characters, emphasizing its ability to work effectively on unstructured text data. Fine-tuning is necessary for applying ByT5 to specific tasks.

Guide: Running Locally

To run ByT5-Large locally, follow these steps:

1. Install Dependencies: Ensure you have Python installed and set up a virtual environment. Install the Hugging Face transformers library.

   pip install transformers torch
   

2. Load the Model: Use the code snippet below to load and infer using the ByT5-Large model.

   from transformers import T5ForConditionalGeneration, AutoTokenizer
   import torch
   
   model = T5ForConditionalGeneration.from_pretrained('google/byt5-large')
   tokenizer = AutoTokenizer.from_pretrained('google/byt5-large')
   
   # Example input
   input_text = ["Life is like a box of chocolates.", "Today is Monday."]
   model_inputs = tokenizer(input_text, padding="longest", return_tensors="pt")
   labels = tokenizer(["La vie est comme une boîte de chocolat.", "Aujourd'hui c'est lundi."], padding="longest", return_tensors="pt").input_ids
   
   # Forward pass
   loss = model(**model_inputs, labels=labels).loss
   

3. Fine-tune the Model: For specific tasks, fine-tuning on task-specific data is advised.

Cloud GPU Recommendation: For efficient training and inference, consider using cloud GPU services like AWS EC2, Google Cloud Platform, or Azure.

License

ByT5-Large is released under the Apache-2.0 license, allowing for open use and modification in compliance with the license terms.