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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- squad |
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model-index: |
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- name: Graphcore/bert-large-uncased-squad |
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results: [] |
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--- |
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# Graphcore/bert-large-uncased-squad |
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This model is a fine-tuned version of [Graphcore/bert-large-uncased](https://huggingface.co/Graphcore/bert-large-uncased) on the squad dataset. |
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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 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. |
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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. |
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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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More information needed |
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## Training and evaluation data |
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[squad dataset](https://huggingface.co/datasets/squad) |
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## Training procedure |
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Model was trained on 16 Graphcore Mk2 IPUs using the [optimum-graphcore](https://github.com/huggingface/optimum-graphcore) library. |
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