t5 base

Introduction

T5-Base is a language model developed by Google, designed to reframe all NLP tasks into a unified text-to-text format. It consists of 220 million parameters and supports multiple languages, including English, French, Romanian, and German. The model is licensed under Apache 2.0.

Architecture

The Text-To-Text Transfer Transformer (T5) allows for a unified framework where input and output are text strings. This differs from BERT-style models which output class labels or spans of input. The T5 framework enables the same model, loss function, and hyperparameters to be used across various NLP tasks.

Training

Training Data

T5-Base was pre-trained on the Colossal Clean Crawled Corpus (C4) and other datasets, using a mixture of unsupervised and supervised tasks. The unsupervised tasks involved denoising objectives, while supervised tasks included sentence acceptability judgment, sentiment analysis, and question answering, among others.

Training Procedure

The training involved converting every language problem into a text-to-text format, systematically exploring transfer learning techniques across dozens of language understanding tasks.

Guide: Running Locally

To run the T5-Base model locally, follow these steps:

1. Install Transformers Library:
   Ensure you have the transformers library installed. You can install it using pip:

   pip install transformers
   

2. Load the Model and Tokenizer:
   Use the following Python code to load the T5 model and tokenizer:

   from transformers import T5Tokenizer, T5Model
   
   tokenizer = T5Tokenizer.from_pretrained("t5-base")
   model = T5Model.from_pretrained("t5-base")
   

3. Tokenize Input:
   Prepare the input text:

   input_ids = tokenizer("Your input text here", return_tensors="pt").input_ids
   decoder_input_ids = tokenizer("Decoder start text", return_tensors="pt").input_ids
   

4. Forward Pass:
   Run a forward pass through the model:

   outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
   last_hidden_states = outputs.last_hidden_state
   

Suggestion: Cloud GPUs

For optimal performance, especially for large datasets or complex tasks, consider using cloud GPU services such as Google Cloud Platform (GCP), AWS, or Azure.

License

T5-Base is released under the Apache 2.0 License, allowing for both commercial and non-commercial use with the proper attribution.