T5 Encoder: Add T5 classification to Transformers

Understand the project

Practical overview

Use it when a Transformers-based Python project needs the repository’s T5 encoder-only sequence- or token-classification classes.

Python developers building text-classification workflows with Hugging Face Transformers.
Researchers exploring T5 encoder-only approaches to sequence classification or named-entity recognition.

Add T5 encoder-only sequence classification to a Transformers workflow.
Explore token classification with T5, including named-entity recognition experiments.
Compare T5 encoder-only results with other Transformer models on GLUE-style tasks.

Before you choose

The project is open source under the Apache-2.0 license, and its documented installation uses pip to install directly from GitHub.

The extension implements both sequence-classification and token-classification classes, while the README’s runnable usage example covers sequence classification.
The README presents the code as preliminary experimentation before possible inclusion in Transformers.
Reported GLUE and NER scores are from single runs; the project warns that results may vary substantially for datasets with few examples.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install the extension and use its documented Transformers example to produce sequence-classification logits.

Install the library from its GitHub repository with the command below.
Documented command
```
python -m pip install git+https://github.com/osainz59/t5-encoder
```
In Python, import t5_encoder and import AutoTokenizer and AutoModelForSequenceClassification from transformers.
Load the tokenizer and sequence-classification model using google/t5-v1_1-base.
Tokenize a sentence with return_tensors="pt", pass the result to the model, and read outputs.logits.

The documented example returns model logits for a sentence supplied to the T5 sequence-classification model.

The example demonstrates sequence classification; it does not document a token-classification workflow.
The repository warns that its reported benchmark results come from single runs and can vary substantially on small datasets.

Evidence and transparency

Source record details

Source availability: Active
Sources checked: 17 October 2026
Original website title: osainz59/t5-encoder · Official homepage title · retained in its source language