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# distilbert-base-uncased-ner-invoiceSenderRecipient_all_inv_20_02

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0344
- eval_precision: 0.8923
- eval_recall: 0.9153
- eval_f1: 0.9036
- eval_accuracy: 0.9903
- eval_runtime: 108.7175
- eval_samples_per_second: 279.532
- eval_steps_per_second: 17.476
- epoch: 3.62
- step: 39000

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.15.0
- Pytorch 1.13.1
- Datasets 2.3.2
- Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-ner-invoiceSenderRecipient_all_inv_20_02", "results": []}]}
transformers pytorch distilbert token-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 




# distilbert-base-uncased-ner-invoiceSenderRecipient_all_inv_20_02

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0344
- eval_precision: 0.8923
- eval_recall: 0.9153
- eval_f1: 0.9036
- eval_accuracy: 0.9903
- eval_runtime: 108.7175
- eval_samples_per_second: 279.532
- eval_steps_per_second: 17.476
- epoch: 3.62
- step: 39000

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.15.0
- Pytorch 1.13.1
- Datasets 2.3.2
- Tokenizers 0.10.3

# distilbert-base-uncased-ner-invoiceSenderRecipient_all_inv_20_02

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0344
- eval_precision: 0.8923
- eval_recall: 0.9153
- eval_f1: 0.9036
- eval_accuracy: 0.9903
- eval_runtime: 108.7175
- eval_samples_per_second: 279.532
- eval_steps_per_second: 17.476
- epoch: 3.62
- step: 39000 ## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.15.0
- Pytorch 1.13.1
- Datasets 2.3.2
- Tokenizers 0.10.3
TAGS
#transformers #pytorch #distilbert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 # distilbert-base-uncased-ner-invoiceSenderRecipient_all_inv_20_02

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0344
- eval_precision: 0.8923
- eval_recall: 0.9153
- eval_f1: 0.9036
- eval_accuracy: 0.9903
- eval_runtime: 108.7175
- eval_samples_per_second: 279.532
- eval_steps_per_second: 17.476
- epoch: 3.62
- step: 39000 ## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.15.0
- Pytorch 1.13.1
- Datasets 2.3.2
- Tokenizers 0.10.3