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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_v2 |
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model-index: |
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- name: roberta-base-squad2 |
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results: [] |
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--- |
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# roberta-base-squad2 |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad_v2 dataset. |
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## Model description |
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RoBERTa is based on BERT pretrain approach but it t evaluates carefully a number of design decisions of BERT pretraining approach so that it found it is undertrained. |
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It suggested a way to improve the performance by training the model longer, with bigger batches over more data, removing the next sentence prediction objectives, training on longer sequences and dynamically changing mask pattern applied to the training data. |
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As a result, it achieves state-of-the-art results on GLUE, RACE and SQuAD and so on on. |
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Paper link : [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://arxiv.org/pdf/1907.11692.pdf) |
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## Training and evaluation data |
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Trained and evaluated on the [squad_v2 dataset](https://huggingface.co/datasets/squad_v2). |
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## Training procedure |
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Trained on 16 Graphcore Mk2 IPUs using [optimum-graphcore](https://github.com/huggingface/optimum-graphcore). |
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Command line: |
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``` |
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python examples/question-answering/run_qa.py \ |
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--ipu_config_name Graphcore/roberta-base-ipu \ |
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--model_name_or_path roberta-base \ |
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--dataset_name squad_v2 \ |
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--version_2_with_negative \ |
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--do_train \ |
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--do_eval \ |
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--num_train_epochs 3 \ |
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--per_device_train_batch_size 4 \ |
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--per_device_eval_batch_size 2 \ |
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--pod_type pod16 \ |
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--learning_rate 7e-5 \ |
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--max_seq_length 384 \ |
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--doc_stride 128 \ |
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--seed 1984 \ |
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--lr_scheduler_type linear \ |
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--loss_scaling 64 \ |
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--weight_decay 0.01 \ |
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--warmup_ratio 0.2 \ |
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--logging_steps 1 \ |
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--save_steps -1 \ |
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--dataloader_num_workers 64 \ |
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--output_dir roberta-base-squad2 \ |
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--overwrite_output_dir \ |
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--push_to_hub |
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``` |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 7e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 2 |
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- seed: 1984 |
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- distributed_type: IPU |
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- total_train_batch_size: 256 |
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- total_eval_batch_size: 40 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.2 |
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- num_epochs: 3.0 |
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- training precision: Mixed Precision |
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### Training results |
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``` |
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***** train metrics ***** |
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epoch = 3.0 |
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train_loss = 0.9982 |
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train_runtime = 0:04:44.21 |
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train_samples = 131823 |
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train_samples_per_second = 1391.43 |
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train_steps_per_second = 5.425 |
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***** eval metrics ***** |
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epoch = 3.0 |
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eval_HasAns_exact = 78.1208 |
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eval_HasAns_f1 = 84.6569 |
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eval_HasAns_total = 5928 |
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eval_NoAns_exact = 82.0353 |
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eval_NoAns_f1 = 82.0353 |
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eval_NoAns_total = 5945 |
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eval_best_exact = 80.0809 |
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eval_best_exact_thresh = 0.0 |
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eval_best_f1 = 83.3442 |
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eval_best_f1_thresh = 0.0 |
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eval_exact = 80.0809 |
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eval_f1 = 83.3442 |
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eval_samples = 12165 |
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eval_total = 11873 |
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``` |
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### Framework versions |
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- Transformers 4.18.0.dev0 |
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- Pytorch 1.10.0+cpu |
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- Datasets 2.0.0 |
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- Tokenizers 0.11.6 |
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