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README.md
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## Model description
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BERT (Bidirectional Encoder Representations from Transformers) is a transformers model which is designed to pretrain bidirectional representations from
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It was trained with two objectives in pretraining : Masked language
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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.
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## Intended uses & limitations
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This model is a pre-trained BERT-Base trained in two phases on the [Graphcore/wikipedia-bert-128](https://huggingface.co/datasets/Graphcore/wikipedia-bert-128) and [Graphcore/wikipedia-bert-512](https://huggingface.co/datasets/Graphcore/wikipedia-bert-512) datasets.
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## Model description
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BERT (Bidirectional Encoder Representations from Transformers) is a transformers model which is designed to pretrain bidirectional representations from unlabelled texts. It enables easy and fast fine-tuning for different downstream tasks such as Sequence Classification, Named Entity Recognition, Question Answering, Multiple Choice and MaskedLM.
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It was trained with two objectives in pretraining : Masked language modelling (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.
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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.
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## Intended uses & limitations
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This model is a pre-trained BERT-Base trained in two phases on the [Graphcore/wikipedia-bert-128](https://huggingface.co/datasets/Graphcore/wikipedia-bert-128) and [Graphcore/wikipedia-bert-512](https://huggingface.co/datasets/Graphcore/wikipedia-bert-512) datasets.
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