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# bert-to-distilbert-NER

This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 5.9063
- eval_precision: 0.0120
- eval_recall: 0.0069
- eval_f1: 0.0088
- eval_accuracy: 0.7600
- eval_runtime: 8.6309
- eval_samples_per_second: 376.671
- eval_steps_per_second: 3.012
- epoch: 1.0
- step: 110

## 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: 0.00023888106906613202
- train_batch_size: 128
- eval_batch_size: 128
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.19.1
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "model-index": [{"name": "bert-to-distilbert-NER", "results": []}]}
transformers pytorch tensorboard bert token-classification generated_from_trainer dataset:conll2003 license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-mit #autotrain_compatible #endpoints_compatible #region-us 




# bert-to-distilbert-NER

This model is a fine-tuned version of dslim/bert-base-NER on the conll2003 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 5.9063
- eval_precision: 0.0120
- eval_recall: 0.0069
- eval_f1: 0.0088
- eval_accuracy: 0.7600
- eval_runtime: 8.6309
- eval_samples_per_second: 376.671
- eval_steps_per_second: 3.012
- epoch: 1.0
- step: 110

## 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: 0.00023888106906613202
- train_batch_size: 128
- eval_batch_size: 128
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.19.1
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1

# bert-to-distilbert-NER

This model is a fine-tuned version of dslim/bert-base-NER on the conll2003 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 5.9063
- eval_precision: 0.0120
- eval_recall: 0.0069
- eval_f1: 0.0088
- eval_accuracy: 0.7600
- eval_runtime: 8.6309
- eval_samples_per_second: 376.671
- eval_steps_per_second: 3.012
- epoch: 1.0
- step: 110 ## 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: 0.00023888106906613202
- train_batch_size: 128
- eval_batch_size: 128
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.19.1
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 # bert-to-distilbert-NER

This model is a fine-tuned version of dslim/bert-base-NER on the conll2003 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 5.9063
- eval_precision: 0.0120
- eval_recall: 0.0069
- eval_f1: 0.0088
- eval_accuracy: 0.7600
- eval_runtime: 8.6309
- eval_samples_per_second: 376.671
- eval_steps_per_second: 3.012
- epoch: 1.0
- step: 110 ## 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: 0.00023888106906613202
- train_batch_size: 128
- eval_batch_size: 128
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.19.1
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-mit #autotrain_compatible #endpoints_compatible #region-us 
# bert-to-distilbert-NER

This model is a fine-tuned version of dslim/bert-base-NER on the conll2003 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 5.9063
- eval_precision: 0.0120
- eval_recall: 0.0069
- eval_f1: 0.0088
- eval_accuracy: 0.7600
- eval_runtime: 8.6309
- eval_samples_per_second: 376.671
- eval_steps_per_second: 3.012
- epoch: 1.0
- step: 110## 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: 0.00023888106906613202
- train_batch_size: 128
- eval_batch_size: 128
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP### Framework versions

- Transformers 4.19.1
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1