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# bert-finetuned-ner

This model is a fine-tuned version of [markusbayer/CySecBERT](https://huggingface.co/markusbayer/CySecBERT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6006
- Precision: 0.8664
- Recall: 0.8691
- F1: 0.8677
- Accuracy: 0.8546

## 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: 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.6569        | 1.0   | 1690 | 0.6027          | 0.8427    | 0.8407 | 0.8417 | 0.8224   |
| 0.4821        | 2.0   | 3380 | 0.5506          | 0.8689    | 0.8663 | 0.8676 | 0.8549   |
| 0.3568        | 3.0   | 5070 | 0.6006          | 0.8664    | 0.8691 | 0.8677 | 0.8546   |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.0+cpu
- Datasets 2.11.0
- Tokenizers 0.13.2

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

bert-finetuned-ner
==================


This model is a fine-tuned version of markusbayer/CySecBERT on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.6006
* Precision: 0.8664
* Recall: 0.8691
* F1: 0.8677
* Accuracy: 0.8546


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: 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: 3


### Training results



### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.0+cpu
* Datasets 2.11.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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: 3 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.0+cpu
* Datasets 2.11.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-unknown #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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: 3 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.0+cpu
* Datasets 2.11.0
* Tokenizers 0.13.2