James Briggs commited on
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1 Parent(s): ab0f88a

Added Graphcore/bert-base-uncased weights and configs

Browse files
README.md ADDED
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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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+ - Graphcore/wikipedia-bert-128
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+ - Graphcore/wikipedia-bert-512
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+ model-index:
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+ - name: Graphcore/bert-base-uncased
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+ results: []
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+ ---
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+
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+ # Graphcore/bert-base-uncased
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+
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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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+
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+ ## Model description
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+
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+ Pre-trained BERT Base model trained on Wikipedia data.
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ Trained on wikipedia datasets:
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+ - [Graphcore/wikipedia-bert-128](https://huggingface.co/datasets/Graphcore/wikipedia-bert-128)
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+ - [Graphcore/wikipedia-bert-512](https://huggingface.co/datasets/Graphcore/wikipedia-bert-512)
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+
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+
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+ ## Training procedure
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+
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+ Trained MLM and NSP pre-training scheme from [Large Batch Optimization for Deep Learning: Training BERT in 76 minutes](https://arxiv.org/abs/1904.00962).
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+ Trained on 16 Graphcore Mk2 IPUs.
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during phase 1 training:
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+ - learning_rate: 0.006
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+ - train_batch_size: 32
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - distributed_type: IPU
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+ - gradient_accumulation_steps: 512
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+ - total_train_batch_size: 65536
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+ - total_eval_batch_size: 128
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+ - optimizer: LAMB
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_ratio: 0.28
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+ - training_steps: 10500
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+ - training precision: Mixed Precision
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+
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+ The following hyperparameters were used during phase 2 training:
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+ - learning_rate: 0.002828
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - distributed_type: IPU
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+ - gradient_accumulation_steps: 512
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+ - total_train_batch_size: 16384
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+ - total_eval_batch_size: 128
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+ - optimizer: LAMB
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_ratio: 0.128
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+ - training_steps: 2038
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+ - training precision: Mixed Precision
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.17.0.dev0
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+ - Pytorch 1.10.0+cpu
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+ - Datasets 1.18.3.dev0
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+ - Tokenizers 0.10.3
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