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# roberta_ner_personal_info

This model is a fine-tuned version of [tner/roberta-large-ontonotes5](https://huggingface.co/tner/roberta-large-ontonotes5) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0401
- Precision: 0.9334
- Recall: 0.9683
- F1: 0.9505
- Accuracy: 0.9915

## 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: 20
- eval_batch_size: 20
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 499  | 0.0443          | 0.9058    | 0.9348 | 0.9201 | 0.9884   |
| 0.2009        | 2.0   | 998  | 0.0410          | 0.9241    | 0.9604 | 0.9419 | 0.9904   |
| 0.0359        | 3.0   | 1497 | 0.0344          | 0.9400    | 0.9632 | 0.9515 | 0.9922   |
| 0.0206        | 4.0   | 1996 | 0.0392          | 0.9379    | 0.9648 | 0.9512 | 0.9916   |
| 0.0141        | 5.0   | 2495 | 0.0377          | 0.9403    | 0.9697 | 0.9547 | 0.9921   |
| 0.0103        | 6.0   | 2994 | 0.0392          | 0.9371    | 0.9681 | 0.9523 | 0.9918   |
| 0.0082        | 7.0   | 3493 | 0.0401          | 0.9334    | 0.9683 | 0.9505 | 0.9915   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "roberta_ner_personal_info", "results": []}]}
transformers pytorch safetensors roberta token-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #roberta #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

roberta\_ner\_personal\_info
============================


This model is a fine-tuned version of tner/roberta-large-ontonotes5 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0401
* Precision: 0.9334
* Recall: 0.9683
* F1: 0.9505
* Accuracy: 0.9915


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: 20
* eval\_batch\_size: 20
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 20
* eval\_batch\_size: 20
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #safetensors #roberta #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 20
* eval\_batch\_size: 20
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3