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# run01-bert-l-uwwm-squadv1.1-sl256-ds128-e2-tbs16

This model is a fine-tuned version of [bert-large-uncased-whole-word-masking](https://huggingface.co/bert-large-uncased-whole-word-masking) on the squad dataset. ONNX and OpenVINO IR are enclosed here.

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure
```bash
NEPOCH=2
TBS=16
EBS=64
SL=256
DS=128

cmd="
python run_qa.py \
    --model_name_or_path ${BASEM} \
    --dataset_name squad \
    --do_eval \
    --do_train \
    --evaluation_strategy steps \
    --eval_steps 500 \
    --learning_rate 3e-5 \
    --fp16 \
    --num_train_epochs $NEPOCH \
    --per_device_eval_batch_size $EBS \
    --per_device_train_batch_size $TBS \
    --max_seq_length $SL \
    --doc_stride $DS \
    --save_steps 1000 \
    --logging_steps 1 \
    --overwrite_output_dir \
    --run_name $RUNID \
    --output_dir $OUTDIR
"
```
### 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
Best checkpoint was at step 11500 but it was not saved. This is final checkpoint (12K+).
```
  eval_exact_match = 86.9347
  eval_f1          = 93.1359
  eval_samples     =   12097
```

### Framework versions

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




{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "run01-bert-l-uwwm-squadv1.1-sl256-ds128-e2-tbs16", "results": []}]}
transformers pytorch onnx bert question-answering generated_from_trainer dataset:squad license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #onnx #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us 




# run01-bert-l-uwwm-squadv1.1-sl256-ds128-e2-tbs16

This model is a fine-tuned version of bert-large-uncased-whole-word-masking on the squad dataset. ONNX and OpenVINO IR are enclosed here.

## 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
Best checkpoint was at step 11500 but it was not saved. This is final checkpoint (12K+).


### Framework versions

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




# run01-bert-l-uwwm-squadv1.1-sl256-ds128-e2-tbs16

This model is a fine-tuned version of bert-large-uncased-whole-word-masking on the squad dataset. ONNX and OpenVINO IR are enclosed here. ## 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
Best checkpoint was at step 11500 but it was not saved. This is final checkpoint (12K+). ### Framework versions

- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #onnx #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us 
 # run01-bert-l-uwwm-squadv1.1-sl256-ds128-e2-tbs16

This model is a fine-tuned version of bert-large-uncased-whole-word-masking on the squad dataset. ONNX and OpenVINO IR are enclosed here. ## 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
Best checkpoint was at step 11500 but it was not saved. This is final checkpoint (12K+). ### Framework versions

- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #onnx #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us 
# run01-bert-l-uwwm-squadv1.1-sl256-ds128-e2-tbs16

This model is a fine-tuned version of bert-large-uncased-whole-word-masking on the squad dataset. ONNX and OpenVINO IR are enclosed here.## 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
Best checkpoint was at step 11500 but it was not saved. This is final checkpoint (12K+).### Framework versions

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