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# training

This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the cynthiachan/FeedRef_10pct dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0810
- 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: 1.0
- Domain Recall: 0.9565
- Domain F1: 0.9778
- Domain Number: 23
- Email Precision: 1.0
- Email Recall: 1.0
- Email F1: 1.0
- Email Number: 3
- Filepath Precision: 0.8841
- Filepath Recall: 0.8788
- Filepath F1: 0.8815
- Filepath Number: 165
- Hostname Precision: 1.0
- Hostname Recall: 1.0
- Hostname F1: 1.0
- Hostname Number: 12
- Ipv4 Precision: 1.0
- Ipv4 Recall: 1.0
- Ipv4 F1: 1.0
- Ipv4 Number: 12
- Md5 Precision: 0.8333
- Md5 Recall: 0.9615
- Md5 F1: 0.8929
- Md5 Number: 52
- Sha1 Precision: 0.6667
- Sha1 Recall: 0.8571
- Sha1 F1: 0.75
- Sha1 Number: 7
- Sha256 Precision: 0.9565
- Sha256 Recall: 1.0
- Sha256 F1: 0.9778
- Sha256 Number: 44
- Uri Precision: 0.0
- Uri Recall: 0.0
- Uri F1: 0.0
- Uri Number: 1
- Overall Precision: 0.9014
- Overall Recall: 0.9201
- Overall F1: 0.9107
- Overall Accuracy: 0.9851

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------------:|:---------------:|:-----------:|:---------------:|:-------------:|:----------:|:------:|:----------:|:------------------------:|:---------------------:|:-----------------:|:---------------------:|:----------------:|:-------------:|:---------:|:-------------:|:---------------:|:------------:|:--------:|:------------:|:------------------:|:---------------:|:-----------:|:---------------:|:------------------:|:---------------:|:-----------:|:---------------:|:--------------:|:-----------:|:-------:|:-----------:|:-------------:|:----------:|:------:|:----------:|:--------------:|:-----------:|:-------:|:-----------:|:----------------:|:-------------:|:---------:|:-------------:|:-------------:|:----------:|:------:|:----------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.3797        | 0.37  | 500  | 0.1998          | 0.0                | 0.0             | 0.0         | 6               | 0.0           | 0.0        | 0.0    | 11         | 0.0                      | 0.0                   | 0.0               | 2                     | 0.0286           | 0.0435        | 0.0345    | 23            | 0.0             | 0.0          | 0.0      | 3            | 0.5108             | 0.7152          | 0.5960      | 165             | 0.1774             | 0.9167          | 0.2973      | 12              | 0.4            | 0.5         | 0.4444  | 12          | 0.3194        | 0.4423     | 0.3710 | 52         | 0.0            | 0.0         | 0.0     | 7           | 0.4588           | 0.8864        | 0.6047    | 44            | 0.0           | 0.0        | 0.0    | 1          | 0.3875            | 0.5858         | 0.4664     | 0.9593           |
| 0.1713        | 0.75  | 1000 | 0.1619          | 0.6                | 0.5             | 0.5455      | 6               | 0.5           | 0.6364     | 0.56   | 11         | 0.0                      | 0.0                   | 0.0               | 2                     | 0.6957           | 0.6957        | 0.6957    | 23            | 0.0             | 0.0          | 0.0      | 3            | 0.6879             | 0.6545          | 0.6708      | 165             | 0.5217             | 1.0             | 0.6857      | 12              | 0.5714         | 1.0         | 0.7273  | 12          | 0.6667        | 0.8846     | 0.7603 | 52         | 0.0            | 0.0         | 0.0     | 7           | 0.7692           | 0.9091        | 0.8333    | 44            | 0.0           | 0.0        | 0.0    | 1          | 0.6685            | 0.7219         | 0.6942     | 0.9664           |
| 0.1152        | 1.12  | 1500 | 0.1096          | 0.8333             | 0.8333          | 0.8333      | 6               | 1.0           | 1.0        | 1.0    | 11         | 0.0                      | 0.0                   | 0.0               | 2                     | 0.7826           | 0.7826        | 0.7826    | 23            | 1.0             | 1.0          | 1.0      | 3            | 0.7202             | 0.8424          | 0.7765      | 165             | 1.0                | 1.0             | 1.0         | 12              | 0.4444         | 1.0         | 0.6154  | 12          | 0.6944        | 0.9615     | 0.8065 | 52         | 0.0            | 0.0         | 0.0     | 7           | 0.8723           | 0.9318        | 0.9011    | 44            | 0.0           | 0.0        | 0.0    | 1          | 0.7312            | 0.8609         | 0.7908     | 0.9751           |
| 0.1089        | 1.5   | 2000 | 0.1243          | 1.0                | 1.0             | 1.0         | 6               | 0.9167        | 1.0        | 0.9565 | 11         | 0.0                      | 0.0                   | 0.0               | 2                     | 0.9048           | 0.8261        | 0.8636    | 23            | 1.0             | 1.0          | 1.0      | 3            | 0.8011             | 0.8788          | 0.8382      | 165             | 0.6667             | 1.0             | 0.8         | 12              | 0.9091         | 0.8333      | 0.8696  | 12          | 0.7812        | 0.9615     | 0.8621 | 52         | 0.0            | 0.0         | 0.0     | 7           | 0.7857           | 1.0           | 0.88      | 44            | 0.0           | 0.0        | 0.0    | 1          | 0.8065            | 0.8876         | 0.8451     | 0.9750           |
| 0.0947        | 1.87  | 2500 | 0.0913          | 0.75               | 1.0             | 0.8571      | 6               | 1.0           | 1.0        | 1.0    | 11         | 0.0                      | 0.0                   | 0.0               | 2                     | 0.8462           | 0.9565        | 0.8980    | 23            | 0.3333          | 0.6667       | 0.4444   | 3            | 0.8035             | 0.8424          | 0.8225      | 165             | 0.6                | 1.0             | 0.7500      | 12              | 1.0            | 1.0         | 1.0     | 12          | 0.7969        | 0.9808     | 0.8793 | 52         | 0.0            | 0.0         | 0.0     | 7           | 0.8302           | 1.0           | 0.9072    | 44            | 0.0           | 0.0        | 0.0    | 1          | 0.7952            | 0.8846         | 0.8375     | 0.9792           |
| 0.0629        | 2.25  | 3000 | 0.0940          | 1.0                | 0.8333          | 0.9091      | 6               | 1.0           | 1.0        | 1.0    | 11         | 0.0                      | 0.0                   | 0.0               | 2                     | 0.9565           | 0.9565        | 0.9565    | 23            | 1.0             | 1.0          | 1.0      | 3            | 0.8671             | 0.8303          | 0.8483      | 165             | 1.0                | 1.0             | 1.0         | 12              | 1.0            | 1.0         | 1.0     | 12          | 0.9273        | 0.9808     | 0.9533 | 52         | 0.25           | 0.1429      | 0.1818  | 7           | 0.8776           | 0.9773        | 0.9247    | 44            | 0.0           | 0.0        | 0.0    | 1          | 0.8946            | 0.8787         | 0.8866     | 0.9825           |
| 0.0442        | 2.62  | 3500 | 0.1012          | 1.0                | 1.0             | 1.0         | 6               | 0.9167        | 1.0        | 0.9565 | 11         | 0.0                      | 0.0                   | 0.0               | 2                     | 0.9091           | 0.8696        | 0.8889    | 23            | 0.75            | 1.0          | 0.8571   | 3            | 0.8182             | 0.8727          | 0.8446      | 165             | 1.0                | 1.0             | 1.0         | 12              | 1.0            | 1.0         | 1.0     | 12          | 0.92          | 0.8846     | 0.9020 | 52         | 0.5            | 1.0         | 0.6667  | 7           | 0.9565           | 1.0           | 0.9778    | 44            | 0.0           | 0.0        | 0.0    | 1          | 0.8616            | 0.9024         | 0.8815     | 0.9818           |
| 0.0401        | 3.0   | 4000 | 0.0810          | 1.0                | 1.0             | 1.0         | 6               | 1.0           | 1.0        | 1.0    | 11         | 0.0                      | 0.0                   | 0.0               | 2                     | 1.0              | 0.9565        | 0.9778    | 23            | 1.0             | 1.0          | 1.0      | 3            | 0.8841             | 0.8788          | 0.8815      | 165             | 1.0                | 1.0             | 1.0         | 12              | 1.0            | 1.0         | 1.0     | 12          | 0.8333        | 0.9615     | 0.8929 | 52         | 0.6667         | 0.8571      | 0.75    | 7           | 0.9565           | 1.0           | 0.9778    | 44            | 0.0           | 0.0        | 0.0    | 1          | 0.9014            | 0.9201         | 0.9107     | 0.9851           |


### Framework versions

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

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

training
========


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


* Loss: 0.0810
* 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: 1.0
* Domain Recall: 0.9565
* Domain F1: 0.9778
* Domain Number: 23
* Email Precision: 1.0
* Email Recall: 1.0
* Email F1: 1.0
* Email Number: 3
* Filepath Precision: 0.8841
* Filepath Recall: 0.8788
* Filepath F1: 0.8815
* Filepath Number: 165
* Hostname Precision: 1.0
* Hostname Recall: 1.0
* Hostname F1: 1.0
* Hostname Number: 12
* Ipv4 Precision: 1.0
* Ipv4 Recall: 1.0
* Ipv4 F1: 1.0
* Ipv4 Number: 12
* Md5 Precision: 0.8333
* Md5 Recall: 0.9615
* Md5 F1: 0.8929
* Md5 Number: 52
* Sha1 Precision: 0.6667
* Sha1 Recall: 0.8571
* Sha1 F1: 0.75
* Sha1 Number: 7
* Sha256 Precision: 0.9565
* Sha256 Recall: 1.0
* Sha256 F1: 0.9778
* Sha256 Number: 44
* Uri Precision: 0.0
* Uri Recall: 0.0
* Uri F1: 0.0
* Uri Number: 1
* Overall Precision: 0.9014
* Overall Recall: 0.9201
* Overall F1: 0.9107
* Overall Accuracy: 0.9851


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 #deberta #token-classification #generated_from_trainer #dataset-cynthiachan/FeedRef_10pct #license-mit #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 #deberta #token-classification #generated_from_trainer #dataset-cynthiachan/FeedRef_10pct #license-mit #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