---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: Graphcore/roberta-base-squad
  results: []
---


# roberta-base-squad

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset.

## Model description
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.  

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. 

As a result, it achieves state-of-the-art results on GLUE, RACE and SQuAD and so on on. 

Paper link : [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://arxiv.org/pdf/1907.11692.pdf)

## Training and evaluation data

Trained and evaluated on the [squad dataset](https://huggingface.co/datasets/squad).

## Training procedure

Trained on 16 Graphcore Mk2 IPUs using [optimum-graphcore](https://github.com/huggingface/optimum-graphcore).

Command line:

```
python examples/question-answering/run_qa.py \
  --ipu_config_name Graphcore/roberta-base-ipu \
  --model_name_or_path roberta-base \
  --dataset_name squad \
  --do_train \
  --do_eval \
  --num_train_epochs 2 \
  --per_device_train_batch_size 4 \
  --per_device_eval_batch_size 2 \
  --pod_type pod16 \
  --learning_rate 6e-5 \
  --max_seq_length 384 \
  --doc_stride 128 \
  --seed 1984 \
  --lr_scheduler_type linear \
  --loss_scaling 64 \
  --weight_decay 0.01 \
  --warmup_ratio 0.25 \
  --logging_steps 1 \
  --save_steps -1 \
  --dataloader_num_workers 64 \
  --output_dir squad_roberta_base \
  --overwrite_output_dir \
  --push_to_hub
```

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 1984
- distributed_type: IPU
- total_train_batch_size: 256
- total_eval_batch_size: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.25
- num_epochs: 2.0
- training precision: Mixed Precision

### Training results

```
***** train metrics *****
  epoch                    =        2.0
  train_loss               =     1.2528
  train_runtime            = 0:02:14.50
  train_samples            =      88568
  train_samples_per_second =   1316.952
  train_steps_per_second   =       5.13

***** eval metrics *****
  epoch            =     2.0
  eval_exact_match = 85.2696
  eval_f1          = 91.7455
  eval_samples     =   10790
```

### Framework versions

- Transformers 4.18.0.dev0
- Pytorch 1.10.0+cpu
- Datasets 2.0.0
- Tokenizers 0.11.6