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# distilbert-bpmn

This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3311
- Precision: 0.7852
- Recall: 0.8375
- F1: 0.8105
- Accuracy: 0.9275

## 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: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 2.0392        | 1.0   | 12   | 1.5999          | 0.2162    | 0.2333 | 0.2244 | 0.5017   |
| 1.3439        | 2.0   | 24   | 1.0197          | 0.3786    | 0.4875 | 0.4262 | 0.7133   |
| 0.8403        | 3.0   | 36   | 0.6398          | 0.5664    | 0.675  | 0.6160 | 0.8333   |
| 0.4941        | 4.0   | 48   | 0.4637          | 0.6775    | 0.7792 | 0.7248 | 0.8765   |
| 0.3227        | 5.0   | 60   | 0.3701          | 0.7262    | 0.7958 | 0.7594 | 0.9041   |
| 0.2206        | 6.0   | 72   | 0.3286          | 0.75      | 0.8125 | 0.78   | 0.9231   |
| 0.1762        | 7.0   | 84   | 0.3330          | 0.7597    | 0.8167 | 0.7871 | 0.9180   |
| 0.1261        | 8.0   | 96   | 0.3159          | 0.7952    | 0.825  | 0.8098 | 0.9266   |
| 0.1121        | 9.0   | 108  | 0.3205          | 0.7860    | 0.8417 | 0.8129 | 0.9275   |
| 0.0902        | 10.0  | 120  | 0.3090          | 0.8071    | 0.8542 | 0.8300 | 0.9326   |
| 0.08          | 11.0  | 132  | 0.3200          | 0.7821    | 0.8375 | 0.8089 | 0.9266   |
| 0.0789        | 12.0  | 144  | 0.3226          | 0.7915    | 0.8542 | 0.8216 | 0.9283   |
| 0.0654        | 13.0  | 156  | 0.3311          | 0.7852    | 0.8375 | 0.8105 | 0.9275   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "distilbert-base-cased", "model-index": [{"name": "distilbert-bpmn", "results": []}]}
transformers pytorch tensorboard safetensors distilbert token-classification generated_from_trainer base_model:distilbert-base-cased license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #distilbert #token-classification #generated_from_trainer #base_model-distilbert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

distilbert-bpmn
===============


This model is a fine-tuned version of distilbert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3311
* Precision: 0.7852
* Recall: 0.8375
* F1: 0.8105
* Accuracy: 0.9275


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: 15


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* 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: 15 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #safetensors #distilbert #token-classification #generated_from_trainer #base_model-distilbert-base-cased #license-apache-2.0 #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: 15 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #safetensors #distilbert #token-classification #generated_from_trainer #base_model-distilbert-base-cased #license-apache-2.0 #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: 15### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2