
<!-- 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. -->

# ner-bert-mini-conll2003

This model is a fine-tuned version of [prajjwal1/bert-mini](https://huggingface.co/prajjwal1/bert-mini) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1007
- Precision: 0.8743
- Recall: 0.8993
- F1: 0.8866
- Accuracy: 0.9745

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results



### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "ner-bert-mini-conll2003", "results": []}]}
transformers pytorch bert token-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 




# ner-bert-mini-conll2003

This model is a fine-tuned version of prajjwal1/bert-mini on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1007
- Precision: 0.8743
- Recall: 0.8993
- F1: 0.8866
- Accuracy: 0.9745

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results



### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2

# ner-bert-mini-conll2003

This model is a fine-tuned version of prajjwal1/bert-mini on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1007
- Precision: 0.8743
- Recall: 0.8993
- F1: 0.8866
- Accuracy: 0.9745 ## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5 ### Training results ### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 # ner-bert-mini-conll2003

This model is a fine-tuned version of prajjwal1/bert-mini on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1007
- Precision: 0.8743
- Recall: 0.8993
- F1: 0.8866
- Accuracy: 0.9745 ## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5 ### Training results ### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
# ner-bert-mini-conll2003

This model is a fine-tuned version of prajjwal1/bert-mini on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1007
- Precision: 0.8743
- Recall: 0.8993
- F1: 0.8866
- Accuracy: 0.9745## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5### Training results### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2