Graphcore and Hugging Face are working together to make training of Transformer models on IPUs fast and easy. Learn more about how to take advantage of the power of Graphcore IPUs to train Transformers models at [hf.co/hardware/graphcore](https://huggingface.co/hardware/graphcore).

# BERT Base model IPU config

This model contains just the `IPUConfig` files for running the BERT base model (e.g. [bert-base-uncased](https://huggingface.co/bert-base-uncased) or [bert-base-cased](https://huggingface.co/bert-base-cased)) on Graphcore IPUs.

**This model contains no model weights, only an IPUConfig.** 

## Model description

BERT (Bidirectional Encoder Representations from Transformers) is a transformers model which is designed to pretrain bidirectional representations from unlabeled texts. It enables easy and fast fine-tuning for different downstream task such as Sequence Classification, Named Entity Recognition, Question Answering, Multiple Choice and MaskedLM.  

It was trained with two objectives in pretraining : Masked language modeling(MLM) and Next sentence prediction(NSP). First, MLM is different from traditional LM which sees the words one after another while BERT allows the model to learn a bidirectional representation.  In addition to MLM, NSP is used for jointly pertaining text-pair representations. 

It reduces the need of many engineering efforts for building task specific architectures through pre-trained representation. And achieves state-of-the-art performance on a large suite of sentence-level and token-level tasks. 

## Usage

```
from optimum.graphcore import IPUConfig
ipu_config = IPUConfig.from_pretrained("Graphcore/bert-base-ipu")
```