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# bert-large-uncased-whole-word-masking-squad2-with-ner-Pistherea-conll2003-with-neg-with-repeat

This model is a fine-tuned version of [deepset/bert-large-uncased-whole-word-masking-squad2](https://huggingface.co/deepset/bert-large-uncased-whole-word-masking-squad2) on the squad_v2 and the conll2003 datasets.

## 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results



### Framework versions

- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3

{"language": ["en"], "license": "cc-by-4.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2", "conll2003"], "model_index": [{"name": "bert-large-uncased-whole-word-masking-squad2-with-ner-Pistherea-conll2003-with-neg-with-repeat", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "squad_v2", "type": "squad_v2", "args": "conll2003"}}, {"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "conll2003", "type": "conll2003"}}]}]}
transformers pytorch bert question-answering generated_from_trainer en dataset:squad_v2 dataset:conll2003 license:cc-by-4.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #en #dataset-squad_v2 #dataset-conll2003 #license-cc-by-4.0 #endpoints_compatible #region-us 




# bert-large-uncased-whole-word-masking-squad2-with-ner-Pistherea-conll2003-with-neg-with-repeat

This model is a fine-tuned version of deepset/bert-large-uncased-whole-word-masking-squad2 on the squad_v2 and the conll2003 datasets.

## 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results



### Framework versions

- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3

# bert-large-uncased-whole-word-masking-squad2-with-ner-Pistherea-conll2003-with-neg-with-repeat

This model is a fine-tuned version of deepset/bert-large-uncased-whole-word-masking-squad2 on the squad_v2 and the conll2003 datasets. ## 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5 ### Training results ### Framework versions

- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #en #dataset-squad_v2 #dataset-conll2003 #license-cc-by-4.0 #endpoints_compatible #region-us 
 # bert-large-uncased-whole-word-masking-squad2-with-ner-Pistherea-conll2003-with-neg-with-repeat

This model is a fine-tuned version of deepset/bert-large-uncased-whole-word-masking-squad2 on the squad_v2 and the conll2003 datasets. ## 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5 ### Training results ### Framework versions

- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #en #dataset-squad_v2 #dataset-conll2003 #license-cc-by-4.0 #endpoints_compatible #region-us 
# bert-large-uncased-whole-word-masking-squad2-with-ner-Pistherea-conll2003-with-neg-with-repeat

This model is a fine-tuned version of deepset/bert-large-uncased-whole-word-masking-squad2 on the squad_v2 and the conll2003 datasets.## 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5### Training results### Framework versions

- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3