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# run05-roberta-large-squadv1.1-sl384-ds128-e2-tbs16

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

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
- mixed_precision_training: Native AMP

### Training results



### Framework versions

- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1

# Train
```bash
python run_qa.py \
    --model_name_or_path roberta-large \
    --dataset_name squad \
    --do_eval \
    --do_train \
    --evaluation_strategy steps \
    --eval_steps 500 \
    --learning_rate 3e-5 \
    --fp16 \
    --num_train_epochs 2 \
    --per_device_eval_batch_size 64 \
    --per_device_train_batch_size 16 \
    --max_seq_length 384 \
    --doc_stride 128 \
    --save_steps 1000 \
    --logging_steps 1 \
    --overwrite_output_dir \
    --run_name $RUNID \
    --output_dir $OUTDIR
```

# Eval
```bash
export CUDA_VISIBLE_DEVICES=0

MODEL=vuiseng9/roberta-l-squadv1.1
OUTDIR=eval-$(basename $MODEL)
WORKDIR=transformers/examples/pytorch/question-answering
cd $WORKDIR

nohup python run_qa.py  \
    --model_name_or_path  $MODEL \
    --dataset_name squad  \
    --do_eval  \
    --per_device_eval_batch_size 16  \
    --max_seq_length 384  \
    --doc_stride 128  \
    --overwrite_output_dir \
    --output_dir $OUTDIR 2>&1 | tee $OUTDIR/run.log &
```
```bash
  eval_exact_match = 88.4674
  eval_f1          = 94.3001
  eval_samples     =   10790
```
{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "run05-roberta-large-squadv1.1-sl384-ds128-e2-tbs16", "results": []}]}
transformers pytorch roberta question-answering generated_from_trainer dataset:squad license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #question-answering #generated_from_trainer #dataset-squad #license-mit #endpoints_compatible #region-us 




# run05-roberta-large-squadv1.1-sl384-ds128-e2-tbs16

This model is a fine-tuned version of roberta-large on the squad dataset.

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
- mixed_precision_training: Native AMP

### Training results



### Framework versions

- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1

# Train


# Eval


# run05-roberta-large-squadv1.1-sl384-ds128-e2-tbs16

This model is a fine-tuned version of roberta-large on the squad dataset. ## Model description

More information needed ## Intended uses & limitations

More information needed ## Training and evaluation data

More information needed ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
- mixed_precision_training: Native AMP ### Training results ### Framework versions

- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1 # Train # Eval
TAGS
#transformers #pytorch #roberta #question-answering #generated_from_trainer #dataset-squad #license-mit #endpoints_compatible #region-us 
 # run05-roberta-large-squadv1.1-sl384-ds128-e2-tbs16

This model is a fine-tuned version of roberta-large on the squad dataset. ## Model description

More information needed ## Intended uses & limitations

More information needed ## Training and evaluation data

More information needed ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
- mixed_precision_training: Native AMP ### Training results ### Framework versions

- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1 # Train # Eval
passage: TAGS
#transformers #pytorch #roberta #question-answering #generated_from_trainer #dataset-squad #license-mit #endpoints_compatible #region-us 
# run05-roberta-large-squadv1.1-sl384-ds128-e2-tbs16

This model is a fine-tuned version of roberta-large on the squad dataset.## Model description

More information needed## Intended uses & limitations

More information needed## Training and evaluation data

More information needed## Training procedure### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
- mixed_precision_training: Native AMP### Training results### Framework versions

- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1# Train# Eval