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# training

This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the cynthiachan/FeedRef_10pct dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1520
- Attackid Precision: 1.0
- Attackid Recall: 1.0
- Attackid F1: 1.0
- Attackid Number: 6
- Cve Precision: 1.0
- Cve Recall: 1.0
- Cve F1: 1.0
- Cve Number: 11
- Defenderthreat Precision: 0.0
- Defenderthreat Recall: 0.0
- Defenderthreat F1: 0.0
- Defenderthreat Number: 2
- Domain Precision: 0.6154
- Domain Recall: 0.6957
- Domain F1: 0.6531
- Domain Number: 23
- Email Precision: 0.5
- Email Recall: 0.6667
- Email F1: 0.5714
- Email Number: 3
- Filepath Precision: 0.7010
- Filepath Recall: 0.8242
- Filepath F1: 0.7577
- Filepath Number: 165
- Hostname Precision: 0.9231
- Hostname Recall: 1.0
- Hostname F1: 0.9600
- Hostname Number: 12
- Ipv4 Precision: 0.7143
- Ipv4 Recall: 0.8333
- Ipv4 F1: 0.7692
- Ipv4 Number: 12
- Md5 Precision: 0.6528
- Md5 Recall: 0.9038
- Md5 F1: 0.7581
- Md5 Number: 52
- Sha1 Precision: 0.0
- Sha1 Recall: 0.0
- Sha1 F1: 0.0
- Sha1 Number: 7
- Sha256 Precision: 0.7692
- Sha256 Recall: 0.9091
- Sha256 F1: 0.8333
- Sha256 Number: 44
- Uri Precision: 0.0
- Uri Recall: 0.0
- Uri F1: 0.0
- Uri Number: 1
- Overall Precision: 0.6897
- Overall Recall: 0.8284
- Overall F1: 0.7527
- Overall Accuracy: 0.9589

## 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: 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: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Attackid Precision | Attackid Recall | Attackid F1 | Attackid Number | Cve Precision | Cve Recall | Cve F1 | Cve Number | Defenderthreat Precision | Defenderthreat Recall | Defenderthreat F1 | Defenderthreat Number | Domain Precision | Domain Recall | Domain F1 | Domain Number | Email Precision | Email Recall | Email F1 | Email Number | Filepath Precision | Filepath Recall | Filepath F1 | Filepath Number | Hostname Precision | Hostname Recall | Hostname F1 | Hostname Number | Ipv4 Precision | Ipv4 Recall | Ipv4 F1 | Ipv4 Number | Md5 Precision | Md5 Recall | Md5 F1 | Md5 Number | Sha1 Precision | Sha1 Recall | Sha1 F1 | Sha1 Number | Sha256 Precision | Sha256 Recall | Sha256 F1 | Sha256 Number | Uri Precision | Uri Recall | Uri F1 | Uri Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------------------:|:---------------:|:-----------:|:---------------:|:-------------:|:----------:|:------:|:----------:|:------------------------:|:---------------------:|:-----------------:|:---------------------:|:----------------:|:-------------:|:---------:|:-------------:|:---------------:|:------------:|:--------:|:------------:|:------------------:|:---------------:|:-----------:|:---------------:|:------------------:|:---------------:|:-----------:|:---------------:|:--------------:|:-----------:|:-------:|:-----------:|:-------------:|:----------:|:------:|:----------:|:--------------:|:-----------:|:-------:|:-----------:|:----------------:|:-------------:|:---------:|:-------------:|:-------------:|:----------:|:------:|:----------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.5093        | 0.37  | 500  | 0.3512          | 0.0                | 0.0             | 0.0         | 6               | 0.0           | 0.0        | 0.0    | 11         | 0.0                      | 0.0                   | 0.0               | 2                     | 0.0              | 0.0           | 0.0       | 23            | 0.0             | 0.0          | 0.0      | 3            | 0.2024             | 0.5091          | 0.2897      | 165             | 0.0                | 0.0             | 0.0         | 12              | 0.0            | 0.0         | 0.0     | 12          | 0.1724        | 0.4808     | 0.2538 | 52         | 0.0            | 0.0         | 0.0     | 7           | 0.3797           | 0.6818        | 0.4878    | 44            | 0.0           | 0.0        | 0.0    | 1          | 0.1844            | 0.4112         | 0.2546     | 0.9163           |
| 0.2742        | 0.75  | 1000 | 0.2719          | 0.0                | 0.0             | 0.0         | 6               | 0.0           | 0.0        | 0.0    | 11         | 0.0                      | 0.0                   | 0.0               | 2                     | 0.4444           | 0.5217        | 0.48      | 23            | 0.0             | 0.0          | 0.0      | 3            | 0.4211             | 0.5333          | 0.4706      | 165             | 0.1111             | 0.25            | 0.1538      | 12              | 0.5            | 0.8333      | 0.625   | 12          | 0.6290        | 0.75       | 0.6842 | 52         | 0.0            | 0.0         | 0.0     | 7           | 0.4444           | 0.8182        | 0.5760    | 44            | 0.0           | 0.0        | 0.0    | 1          | 0.4322            | 0.5562         | 0.4864     | 0.9340           |
| 0.2072        | 1.12  | 1500 | 0.2008          | 0.0                | 0.0             | 0.0         | 6               | 0.2308        | 0.2727     | 0.2500 | 11         | 0.0                      | 0.0                   | 0.0               | 2                     | 0.6842           | 0.5652        | 0.6190    | 23            | 0.0             | 0.0          | 0.0      | 3            | 0.4885             | 0.7758          | 0.5995      | 165             | 0.7857             | 0.9167          | 0.8462      | 12              | 0.75           | 0.75        | 0.75    | 12          | 0.6026        | 0.9038     | 0.7231 | 52         | 0.0            | 0.0         | 0.0     | 7           | 0.5970           | 0.9091        | 0.7207    | 44            | 0.0           | 0.0        | 0.0    | 1          | 0.5363            | 0.7426         | 0.6228     | 0.9484           |
| 0.1861        | 1.5   | 2000 | 0.2101          | 0.0                | 0.0             | 0.0         | 6               | 0.9091        | 0.9091     | 0.9091 | 11         | 0.0                      | 0.0                   | 0.0               | 2                     | 0.5926           | 0.6957        | 0.6400    | 23            | 0.5             | 0.3333       | 0.4      | 3            | 0.6345             | 0.7576          | 0.6906      | 165             | 0.7333             | 0.9167          | 0.8148      | 12              | 0.8182         | 0.75        | 0.7826  | 12          | 0.6618        | 0.8654     | 0.75   | 52         | 0.0            | 0.0         | 0.0     | 7           | 0.525            | 0.9545        | 0.6774    | 44            | 0.0           | 0.0        | 0.0    | 1          | 0.6181            | 0.7663         | 0.6843     | 0.9495           |
| 0.1888        | 1.87  | 2500 | 0.1689          | 1.0                | 1.0             | 1.0         | 6               | 0.8182        | 0.8182     | 0.8182 | 11         | 0.0                      | 0.0                   | 0.0               | 2                     | 0.6818           | 0.6522        | 0.6667    | 23            | 0.0             | 0.0          | 0.0      | 3            | 0.5806             | 0.7636          | 0.6597      | 165             | 0.8462             | 0.9167          | 0.8800      | 12              | 0.8182         | 0.75        | 0.7826  | 12          | 0.6486        | 0.9231     | 0.7619 | 52         | 0.0            | 0.0         | 0.0     | 7           | 0.6667           | 0.8636        | 0.7525    | 44            | 0.0           | 0.0        | 0.0    | 1          | 0.6329            | 0.7751         | 0.6968     | 0.9487           |
| 0.1409        | 2.25  | 3000 | 0.1520          | 1.0                | 1.0             | 1.0         | 6               | 1.0           | 1.0        | 1.0    | 11         | 0.0                      | 0.0                   | 0.0               | 2                     | 0.6154           | 0.6957        | 0.6531    | 23            | 0.5             | 0.6667       | 0.5714   | 3            | 0.7010             | 0.8242          | 0.7577      | 165             | 0.9231             | 1.0             | 0.9600      | 12              | 0.7143         | 0.8333      | 0.7692  | 12          | 0.6528        | 0.9038     | 0.7581 | 52         | 0.0            | 0.0         | 0.0     | 7           | 0.7692           | 0.9091        | 0.8333    | 44            | 0.0           | 0.0        | 0.0    | 1          | 0.6897            | 0.8284         | 0.7527     | 0.9589           |
| 0.1248        | 2.62  | 3500 | 0.1716          | 0.8571             | 1.0             | 0.9231      | 6               | 1.0           | 1.0        | 1.0    | 11         | 0.0                      | 0.0                   | 0.0               | 2                     | 0.84             | 0.9130        | 0.8750    | 23            | 0.6667          | 0.6667       | 0.6667   | 3            | 0.8155             | 0.8303          | 0.8228      | 165             | 0.8571             | 1.0             | 0.9231      | 12              | 0.75           | 1.0         | 0.8571  | 12          | 0.7031        | 0.8654     | 0.7759 | 52         | 0.0            | 0.0         | 0.0     | 7           | 0.7593           | 0.9318        | 0.8367    | 44            | 0.0           | 0.0        | 0.0    | 1          | 0.7928            | 0.8491         | 0.82       | 0.9583           |
| 0.1073        | 3.0   | 4000 | 0.1532          | 0.8571             | 1.0             | 0.9231      | 6               | 1.0           | 1.0        | 1.0    | 11         | 0.0                      | 0.0                   | 0.0               | 2                     | 0.84             | 0.9130        | 0.8750    | 23            | 0.6667          | 0.6667       | 0.6667   | 3            | 0.7705             | 0.8545          | 0.8103      | 165             | 0.8571             | 1.0             | 0.9231      | 12              | 0.7059         | 1.0         | 0.8276  | 12          | 0.7313        | 0.9423     | 0.8235 | 52         | 0.0            | 0.0         | 0.0     | 7           | 0.7241           | 0.9545        | 0.8235    | 44            | 0.0           | 0.0        | 0.0    | 1          | 0.7688            | 0.8757         | 0.8188     | 0.9618           |


### Framework versions

- Transformers 4.21.2
- Pytorch 1.12.1+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["cynthiachan/FeedRef_10pct"], "model-index": [{"name": "training", "results": []}]}
transformers pytorch electra token-classification generated_from_trainer dataset:cynthiachan/FeedRef_10pct license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #electra #token-classification #generated_from_trainer #dataset-cynthiachan/FeedRef_10pct #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

training
========


This model is a fine-tuned version of google/electra-base-discriminator on the cynthiachan/FeedRef\_10pct dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1520
* Attackid Precision: 1.0
* Attackid Recall: 1.0
* Attackid F1: 1.0
* Attackid Number: 6
* Cve Precision: 1.0
* Cve Recall: 1.0
* Cve F1: 1.0
* Cve Number: 11
* Defenderthreat Precision: 0.0
* Defenderthreat Recall: 0.0
* Defenderthreat F1: 0.0
* Defenderthreat Number: 2
* Domain Precision: 0.6154
* Domain Recall: 0.6957
* Domain F1: 0.6531
* Domain Number: 23
* Email Precision: 0.5
* Email Recall: 0.6667
* Email F1: 0.5714
* Email Number: 3
* Filepath Precision: 0.7010
* Filepath Recall: 0.8242
* Filepath F1: 0.7577
* Filepath Number: 165
* Hostname Precision: 0.9231
* Hostname Recall: 1.0
* Hostname F1: 0.9600
* Hostname Number: 12
* Ipv4 Precision: 0.7143
* Ipv4 Recall: 0.8333
* Ipv4 F1: 0.7692
* Ipv4 Number: 12
* Md5 Precision: 0.6528
* Md5 Recall: 0.9038
* Md5 F1: 0.7581
* Md5 Number: 52
* Sha1 Precision: 0.0
* Sha1 Recall: 0.0
* Sha1 F1: 0.0
* Sha1 Number: 7
* Sha256 Precision: 0.7692
* Sha256 Recall: 0.9091
* Sha256 F1: 0.8333
* Sha256 Number: 44
* Uri Precision: 0.0
* Uri Recall: 0.0
* Uri F1: 0.0
* Uri Number: 1
* Overall Precision: 0.6897
* Overall Recall: 0.8284
* Overall F1: 0.7527
* Overall Accuracy: 0.9589


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: 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: 3.0


### Training results



### Framework versions


* Transformers 4.21.2
* Pytorch 1.12.1+cu102
* Datasets 2.4.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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: 3.0 ### Training results ### Framework versions


* Transformers 4.21.2
* Pytorch 1.12.1+cu102
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #electra #token-classification #generated_from_trainer #dataset-cynthiachan/FeedRef_10pct #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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: 3.0 ### Training results ### Framework versions


* Transformers 4.21.2
* Pytorch 1.12.1+cu102
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #electra #token-classification #generated_from_trainer #dataset-cynthiachan/FeedRef_10pct #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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: 3.0### Training results### Framework versions


* Transformers 4.21.2
* Pytorch 1.12.1+cu102
* Datasets 2.4.0
* Tokenizers 0.12.1