
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# distilbert-base-uncased-qa-with-ner

This model is a fine-tuned version of [andi611/distilbert-base-uncased-qa](https://huggingface.co/andi611/distilbert-base-uncased-qa) on the conll2003 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results



### Framework versions

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

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "model_index": [{"name": "distilbert-base-uncased-qa-with-ner", "results": [{"task": {"name": "Question Answering", "type": "question-answering"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}}]}]}
transformers pytorch distilbert question-answering generated_from_trainer dataset:conll2003 license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #endpoints_compatible #region-us 




# distilbert-base-uncased-qa-with-ner

This model is a fine-tuned version of andi611/distilbert-base-uncased-qa on the conll2003 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results



### Framework versions

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

# distilbert-base-uncased-qa-with-ner

This model is a fine-tuned version of andi611/distilbert-base-uncased-qa on the conll2003 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0 ### Training results ### Framework versions

- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
TAGS
#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #endpoints_compatible #region-us 
 # distilbert-base-uncased-qa-with-ner

This model is a fine-tuned version of andi611/distilbert-base-uncased-qa on the conll2003 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0 ### 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 #distilbert #question-answering #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #endpoints_compatible #region-us 
# distilbert-base-uncased-qa-with-ner

This model is a fine-tuned version of andi611/distilbert-base-uncased-qa on the conll2003 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0### Training results### Framework versions

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