# Extract QA Model (SQuAD2.0)

## Model Information

Pretrained model: google/bert_uncased_L-12_H-768_A-12

## Training Hyperparameters

```Python
epochs = 2
batch_size = 24
learning_rate = 3e-5

max_seq_length = 384
doc_stride = 128
max_query_length = 256
```

## Latest Result
```json
// 11 Apr 2023, 11:08 (Staging Epoch 1, total epoch 4, Correct Tokenizer)
{
    "exact": 74.43780005053483,
    "f1": 77.49749091378419,
    "total": 11873,
    "HasAns_exact": 73.73481781376518,
    "HasAns_f1": 79.86297395738177,
    "HasAns_total": 5928,
    "NoAns_exact": 75.13877207737595,
    "NoAns_f1": 75.13877207737595,
    "NoAns_total": 5945,
    "best_exact": 74.43780005053483,
    "best_exact_thresh": 0.0,
    "best_f1": 77.49749091378418,
    "best_f1_thresh": 0.0
}
```

```json
// 11 Apr 2023, 09:38 (Staging Epoch 3, total epoch 4, Correct Tokenizer)
{
    "exact": 73.99983155057694,
    "f1": 77.23749498407376,
    "total": 11873,
    "HasAns_exact": 72.85762483130904,
    "HasAns_f1": 79.3422364955984,
    "HasAns_total": 5928,
    "NoAns_exact": 75.13877207737595,
    "NoAns_f1": 75.13877207737595,
    "NoAns_total": 5945,
    "best_exact": 73.99983155057694,
    "best_exact_thresh": 0.0,
    "best_f1": 77.23749498407373,
    "best_f1_thresh": 0.0
}
```


```json
// 24 Mar 2023, 19:57 (Invalid Tokenizer)
{
    "exact": 74.12616861787248,
    "f1": 77.34212395572948,
    "total": 11873,
    "HasAns_exact": 72.72267206477733,
    "HasAns_f1": 79.16380528447645,
    "HasAns_total": 5928,
    "NoAns_exact": 75.52565180824222,
    "NoAns_f1": 75.52565180824222,
    "NoAns_total": 5945,
    "best_exact": 74.12616861787248,
    "best_exact_thresh": 0.0,
    "best_f1": 77.34212395572948,
    "best_f1_thresh": 0.0
}
```

```json
// 23 Mar 2023,  (Invalid Tokenizer)
{
    "exact": 73.77242482944496,
    "f1": 76.83752039897598,
    "total": 11873,
    "HasAns_exact": 71.72739541160594,
    "HasAns_f1": 77.86637646711235,
    "HasAns_total": 5928,
    "NoAns_exact": 75.81160639192599,
    "NoAns_f1": 75.81160639192599,
    "NoAns_total": 5945,
    "best_exact": 73.77242482944496,
    "best_exact_thresh": 0.0,
    "best_f1": 76.83752039897604,
    "best_f1_thresh": 0.0
}
```
{"language": ["en"], "license": "mit", "tags": ["Bert", "SQuAD2.0", "SQuAD"], "datasets": ["squad_v2"], "pipeline_tag": "question-answering"}
transformers pytorch safetensors bert question-answering Bert SQuAD2.0 SQuAD en dataset:squad_v2 license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #bert #question-answering #Bert #SQuAD2.0 #SQuAD #en #dataset-squad_v2 #license-mit #endpoints_compatible #region-us 

# Extract QA Model (SQuAD2.0)

## Model Information

Pretrained model: google/bert_uncased_L-12_H-768_A-12

## Training Hyperparameters



## Latest Result








# Extract QA Model (SQuAD2.0) ## Model Information

Pretrained model: google/bert_uncased_L-12_H-768_A-12 ## Training Hyperparameters ## Latest Result
TAGS
#transformers #pytorch #safetensors #bert #question-answering #Bert #SQuAD2.0 #SQuAD #en #dataset-squad_v2 #license-mit #endpoints_compatible #region-us 
 # Extract QA Model (SQuAD2.0) ## Model Information

Pretrained model: google/bert_uncased_L-12_H-768_A-12 ## Training Hyperparameters ## Latest Result
TAGS
#transformers #pytorch #safetensors #bert #question-answering #Bert #SQuAD2.0 #SQuAD #en #dataset-squad_v2 #license-mit #endpoints_compatible #region-us 
# Extract QA Model (SQuAD2.0)## Model Information

Pretrained model: google/bert_uncased_L-12_H-768_A-12## Training Hyperparameters## Latest Result