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# test-ner

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0398
- Precision: 0.9468
- Recall: 0.9579
- F1: 0.9523
- Accuracy: 0.9921

## 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: 1
- eval_batch_size: 2
- seed: 42
- distributed_type: IPU
- total_train_batch_size: 16
- total_eval_batch_size: 10
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- training precision: Mixed Precision

### Training results



### Framework versions

- Transformers 4.20.0
- Pytorch 1.10.0+cpu
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "test-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}, "metrics": [{"type": "precision", "value": 0.9467731204258151, "name": "Precision"}, {"type": "recall", "value": 0.9579266240323123, "name": "Recall"}, {"type": "f1", "value": 0.952317215994646, "name": "F1"}, {"type": "accuracy", "value": 0.9920953233908337, "name": "Accuracy"}]}]}]}
transformers pytorch optimum_graphcore roberta token-classification generated_from_trainer dataset:conll2003 license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #optimum_graphcore #roberta #token-classification #generated_from_trainer #dataset-conll2003 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 




# test-ner

This model is a fine-tuned version of roberta-base on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0398
- Precision: 0.9468
- Recall: 0.9579
- F1: 0.9523
- Accuracy: 0.9921

## 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: 1
- eval_batch_size: 2
- seed: 42
- distributed_type: IPU
- total_train_batch_size: 16
- total_eval_batch_size: 10
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- training precision: Mixed Precision

### Training results



### Framework versions

- Transformers 4.20.0
- Pytorch 1.10.0+cpu
- Datasets 2.4.0
- Tokenizers 0.12.1

# test-ner

This model is a fine-tuned version of roberta-base on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0398
- Precision: 0.9468
- Recall: 0.9579
- F1: 0.9523
- Accuracy: 0.9921 ## 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: 1
- eval_batch_size: 2
- seed: 42
- distributed_type: IPU
- total_train_batch_size: 16
- total_eval_batch_size: 10
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- training precision: Mixed Precision ### Training results ### Framework versions

- Transformers 4.20.0
- Pytorch 1.10.0+cpu
- Datasets 2.4.0
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #optimum_graphcore #roberta #token-classification #generated_from_trainer #dataset-conll2003 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
 # test-ner

This model is a fine-tuned version of roberta-base on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0398
- Precision: 0.9468
- Recall: 0.9579
- F1: 0.9523
- Accuracy: 0.9921 ## 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: 1
- eval_batch_size: 2
- seed: 42
- distributed_type: IPU
- total_train_batch_size: 16
- total_eval_batch_size: 10
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- training precision: Mixed Precision ### Training results ### Framework versions

- Transformers 4.20.0
- Pytorch 1.10.0+cpu
- Datasets 2.4.0
- Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #optimum_graphcore #roberta #token-classification #generated_from_trainer #dataset-conll2003 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
# test-ner

This model is a fine-tuned version of roberta-base on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0398
- Precision: 0.9468
- Recall: 0.9579
- F1: 0.9523
- Accuracy: 0.9921## 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: 1
- eval_batch_size: 2
- seed: 42
- distributed_type: IPU
- total_train_batch_size: 16
- total_eval_batch_size: 10
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- training precision: Mixed Precision### Training results### Framework versions

- Transformers 4.20.0
- Pytorch 1.10.0+cpu
- Datasets 2.4.0
- Tokenizers 0.12.1