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# bert-base-uncased-squad-v1

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad dataset.
It was finetuned following the [Transformers Question Answering example](https://github.com/huggingface/transformers/tree/main/examples/pytorch/question-answering#fine-tuning-bert-on-squad10):

```
python run_qa.py \
  --model_name_or_path bert-base-uncased \
  --dataset_name squad \
  --do_train \
  --do_eval \
  --per_device_train_batch_size 12 \
  --learning_rate 3e-5 \
  --num_train_epochs 2 \
  --max_seq_length 384 \
  --doc_stride 128 \
```


### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0

### Training results

```
***** eval metrics *****
  epoch                   =        2.0
  eval_exact_match        =    81.3434
  eval_f1                 =    88.7002
  eval_samples            =      10784
```

### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "base_model": "bert-base-uncased", "model-index": [{"name": "bert-base-uncased-squad-v1", "results": []}]}
transformers pytorch bert question-answering generated_from_trainer dataset:squad base_model:bert-base-uncased license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #base_model-bert-base-uncased #license-apache-2.0 #endpoints_compatible #region-us 




# bert-base-uncased-squad-v1

This model is a fine-tuned version of bert-base-uncased on the squad dataset.
It was finetuned following the Transformers Question Answering example:




### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0

### Training results



### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2

# bert-base-uncased-squad-v1

This model is a fine-tuned version of bert-base-uncased on the squad dataset.
It was finetuned following the Transformers Question Answering example: ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0 ### Training results ### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #base_model-bert-base-uncased #license-apache-2.0 #endpoints_compatible #region-us 
 # bert-base-uncased-squad-v1

This model is a fine-tuned version of bert-base-uncased on the squad dataset.
It was finetuned following the Transformers Question Answering example: ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0 ### Training results ### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #base_model-bert-base-uncased #license-apache-2.0 #endpoints_compatible #region-us 
# bert-base-uncased-squad-v1

This model is a fine-tuned version of bert-base-uncased on the squad dataset.
It was finetuned following the Transformers Question Answering example:### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0### Training results### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2