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# bert-base-cased-ner-conll2003

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0355
- Precision: 0.9438
- Recall: 0.9525
- F1: 0.9482
- Accuracy: 0.9911

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
- mixed_precision_training: Native AMP

### Training results



### Framework versions

- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.1.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "bert-base-cased", "model-index": [{"name": "bert-base-cased-ner-conll2003", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}, "metrics": [{"type": "precision", "value": 0.9438052359513089, "name": "Precision"}, {"type": "recall", "value": 0.9525412319084483, "name": "Recall"}, {"type": "f1", "value": 0.9481531116508919, "name": "F1"}, {"type": "accuracy", "value": 0.9910634321093416, "name": "Accuracy"}]}, {"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "config": "conll2003", "split": "test"}, "metrics": [{"type": "accuracy", "value": 0.9116307653519484, "name": "Accuracy", "verified": true}, {"type": "precision", "value": 0.9366103911345081, "name": "Precision", "verified": true}, {"type": "recall", "value": 0.9262526113340186, "name": "Recall", "verified": true}, {"type": "f1", "value": 0.9314027058794109, "name": "F1", "verified": true}, {"type": "loss", "value": 0.4366346299648285, "name": "loss", "verified": true}]}]}]}
transformers pytorch tensorboard bert token-classification generated_from_trainer dataset:conll2003 base_model:bert-base-cased license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #base_model-bert-base-cased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 




# bert-base-cased-ner-conll2003

This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0355
- Precision: 0.9438
- Recall: 0.9525
- F1: 0.9482
- Accuracy: 0.9911

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
- mixed_precision_training: Native AMP

### Training results



### Framework versions

- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.1.0
- Tokenizers 0.12.1

# bert-base-cased-ner-conll2003

This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0355
- Precision: 0.9438
- Recall: 0.9525
- F1: 0.9482
- Accuracy: 0.9911 ## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
- mixed_precision_training: Native AMP ### Training results ### Framework versions

- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.1.0
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #base_model-bert-base-cased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 # bert-base-cased-ner-conll2003

This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0355
- Precision: 0.9438
- Recall: 0.9525
- F1: 0.9482
- Accuracy: 0.9911 ## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
- mixed_precision_training: Native AMP ### Training results ### Framework versions

- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.1.0
- Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #base_model-bert-base-cased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
# bert-base-cased-ner-conll2003

This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0355
- Precision: 0.9438
- Recall: 0.9525
- F1: 0.9482
- Accuracy: 0.9911## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
- mixed_precision_training: Native AMP### Training results### Framework versions

- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.1.0
- Tokenizers 0.12.1