Graphcore/sentence-t5-base

Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at hf.co/hardware/graphcore.

Through HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project.

Model description

(source: https://huggingface.co/sentence-transformers/sentence-t5-base)

Sentence-t5 is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model works well for sentence similarity tasks, but doesn't perform that well for semantic search tasks.

This model was converted from the Tensorflow model st5-base-1 to PyTorch. When using this model, have a look at the publication: Sentence-T5: Scalable sentence encoders from pre-trained text-to-text models. The tfhub model and this PyTorch model can produce slightly different embeddings, however, when run on the same benchmarks, they produce identical results.

The model uses only the encoder from a T5-base model. The weights are stored in FP16.

Intended uses & limitations

This model contains just the IPUConfig files for running the sentence-t5-base model (e.g. sentence-transformers/sentence-t5-base) on Graphcore IPUs.

This model contains no model weights, only an IPUConfig.

Usage

from optimum.graphcore import IPUConfig
from transformers import T5EncoderModel
ipu_config = IPUConfig.from_pretrained("Graphcore/sentence-t5-base")
model = T5EncoderModel.from_pretrained("sentence-transformers/sentence-t5-base")