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# training

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the cynthiachan/FeedRef_10pct dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1033
- 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: 0.8636
- Domain Recall: 0.8261
- Domain F1: 0.8444
- Domain Number: 23
- Email Precision: 1.0
- Email Recall: 1.0
- Email F1: 1.0
- Email Number: 3
- Filepath Precision: 0.8108
- Filepath Recall: 0.9091
- Filepath F1: 0.8571
- Filepath Number: 165
- Hostname Precision: 0.9231
- Hostname Recall: 1.0
- Hostname F1: 0.9600
- Hostname Number: 12
- Ipv4 Precision: 0.9167
- Ipv4 Recall: 0.9167
- Ipv4 F1: 0.9167
- Ipv4 Number: 12
- Md5 Precision: 0.875
- Md5 Recall: 0.9423
- Md5 F1: 0.9074
- Md5 Number: 52
- Sha1 Precision: 0.75
- Sha1 Recall: 0.8571
- Sha1 F1: 0.8000
- Sha1 Number: 7
- Sha256 Precision: 0.8
- Sha256 Recall: 1.0
- Sha256 F1: 0.8889
- Sha256 Number: 44
- Uri Precision: 0.0
- Uri Recall: 0.0
- Uri F1: 0.0
- Uri Number: 1
- Overall Precision: 0.8383
- Overall Recall: 0.9201
- Overall F1: 0.8773
- Overall Accuracy: 0.9816

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------------:|:---------------:|:-----------:|:---------------:|:-------------:|:----------:|:------:|:----------:|:------------------------:|:---------------------:|:-----------------:|:---------------------:|:----------------:|:-------------:|:---------:|:-------------:|:---------------:|:------------:|:--------:|:------------:|:------------------:|:---------------:|:-----------:|:---------------:|:------------------:|:---------------:|:-----------:|:---------------:|:--------------:|:-----------:|:-------:|:-----------:|:-------------:|:----------:|:------:|:----------:|:--------------:|:-----------:|:-------:|:-----------:|:----------------:|:-------------:|:---------:|:-------------:|:-------------:|:----------:|:------:|:----------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.4353        | 0.37  | 500  | 0.3525          | 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            | 0.3984             | 0.6182          | 0.4846      | 165             | 0.0714             | 0.3333          | 0.1176      | 12              | 0.0            | 0.0         | 0.0     | 12          | 0.8936        | 0.8077     | 0.8485 | 52         | 0.0            | 0.0         | 0.0     | 7           | 0.4937           | 0.8864        | 0.6341    | 44            | 0.0           | 0.0        | 0.0    | 1          | 0.4156            | 0.5533         | 0.4746     | 0.9459           |
| 0.2089        | 0.75  | 1000 | 0.1812          | 0.0                | 0.0             | 0.0         | 6               | 0.9           | 0.8182     | 0.8571 | 11         | 0.0                      | 0.0                   | 0.0               | 2                     | 0.15             | 0.2609        | 0.1905    | 23            | 0.0             | 0.0          | 0.0      | 3            | 0.6432             | 0.7758          | 0.7033      | 165             | 0.0                | 0.0             | 0.0         | 12              | 0.6471         | 0.9167      | 0.7586  | 12          | 0.7143        | 0.8654     | 0.7826 | 52         | 0.0            | 0.0         | 0.0     | 7           | 0.5286           | 0.8409        | 0.6491    | 44            | 0.0           | 0.0        | 0.0    | 1          | 0.5315            | 0.6982         | 0.6036     | 0.9626           |
| 0.1453        | 1.12  | 1500 | 0.1374          | 0.75               | 0.5             | 0.6         | 6               | 0.9167        | 1.0        | 0.9565 | 11         | 0.0                      | 0.0                   | 0.0               | 2                     | 0.5135           | 0.8261        | 0.6333    | 23            | 0.0             | 0.0          | 0.0      | 3            | 0.6863             | 0.8485          | 0.7588      | 165             | 0.7                | 0.5833          | 0.6364      | 12              | 0.6667         | 0.6667      | 0.6667  | 12          | 0.8167        | 0.9423     | 0.8750 | 52         | 0.0            | 0.0         | 0.0     | 7           | 0.8333           | 0.9091        | 0.8696    | 44            | 0.0           | 0.0        | 0.0    | 1          | 0.7048            | 0.8195         | 0.7579     | 0.9745           |
| 0.1277        | 1.5   | 2000 | 0.1400          | 1.0                | 1.0             | 1.0         | 6               | 1.0           | 1.0        | 1.0    | 11         | 0.0                      | 0.0                   | 0.0               | 2                     | 0.7273           | 0.6957        | 0.7111    | 23            | 0.2             | 0.3333       | 0.25     | 3            | 0.7181             | 0.8182          | 0.7649      | 165             | 0.9167             | 0.9167          | 0.9167      | 12              | 0.7857         | 0.9167      | 0.8462  | 12          | 0.8167        | 0.9423     | 0.8750 | 52         | 0.0            | 0.0         | 0.0     | 7           | 0.8302           | 1.0           | 0.9072    | 44            | 0.0           | 0.0        | 0.0    | 1          | 0.7634            | 0.8402         | 0.8000     | 0.9735           |
| 0.1074        | 1.87  | 2500 | 0.1101          | 1.0                | 1.0             | 1.0         | 6               | 1.0           | 1.0        | 1.0    | 11         | 0.0                      | 0.0                   | 0.0               | 2                     | 0.72             | 0.7826        | 0.7500    | 23            | 0.2857          | 0.6667       | 0.4      | 3            | 0.7554             | 0.8424          | 0.7966      | 165             | 0.8571             | 1.0             | 0.9231      | 12              | 0.8182         | 0.75        | 0.7826  | 12          | 0.9259        | 0.9615     | 0.9434 | 52         | 0.0            | 0.0         | 0.0     | 7           | 0.6833           | 0.9318        | 0.7885    | 44            | 0.0           | 0.0        | 0.0    | 1          | 0.7660            | 0.8521         | 0.8067     | 0.9762           |
| 0.0758        | 2.25  | 3000 | 0.1161          | 1.0                | 1.0             | 1.0         | 6               | 1.0           | 1.0        | 1.0    | 11         | 0.0                      | 0.0                   | 0.0               | 2                     | 0.9091           | 0.8696        | 0.8889    | 23            | 0.5             | 0.6667       | 0.5714   | 3            | 0.8251             | 0.9152          | 0.8678      | 165             | 1.0                | 1.0             | 1.0         | 12              | 1.0            | 0.6667      | 0.8     | 12          | 0.9259        | 0.9615     | 0.9434 | 52         | 1.0            | 0.5714      | 0.7273  | 7           | 0.8958           | 0.9773        | 0.9348    | 44            | 0.0           | 0.0        | 0.0    | 1          | 0.8722            | 0.9083         | 0.8899     | 0.9814           |
| 0.064         | 2.62  | 3500 | 0.1275          | 1.0                | 1.0             | 1.0         | 6               | 0.8333        | 0.9091     | 0.8696 | 11         | 0.0                      | 0.0                   | 0.0               | 2                     | 0.8947           | 0.7391        | 0.8095    | 23            | 1.0             | 1.0          | 1.0      | 3            | 0.8418             | 0.9030          | 0.8713      | 165             | 0.8571             | 1.0             | 0.9231      | 12              | 1.0            | 0.75        | 0.8571  | 12          | 0.9245        | 0.9423     | 0.9333 | 52         | 0.6667         | 0.5714      | 0.6154  | 7           | 0.8113           | 0.9773        | 0.8866    | 44            | 0.0           | 0.0        | 0.0    | 1          | 0.8580            | 0.8935         | 0.8754     | 0.9793           |
| 0.0522        | 3.0   | 4000 | 0.1033          | 1.0                | 1.0             | 1.0         | 6               | 1.0           | 1.0        | 1.0    | 11         | 0.0                      | 0.0                   | 0.0               | 2                     | 0.8636           | 0.8261        | 0.8444    | 23            | 1.0             | 1.0          | 1.0      | 3            | 0.8108             | 0.9091          | 0.8571      | 165             | 0.9231             | 1.0             | 0.9600      | 12              | 0.9167         | 0.9167      | 0.9167  | 12          | 0.875         | 0.9423     | 0.9074 | 52         | 0.75           | 0.8571      | 0.8000  | 7           | 0.8              | 1.0           | 0.8889    | 44            | 0.0           | 0.0        | 0.0    | 1          | 0.8383            | 0.9201         | 0.8773     | 0.9816           |


### 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 roberta token-classification generated_from_trainer dataset:cynthiachan/FeedRef_10pct license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #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 roberta-base on the cynthiachan/FeedRef\_10pct dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1033
* 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: 0.8636
* Domain Recall: 0.8261
* Domain F1: 0.8444
* Domain Number: 23
* Email Precision: 1.0
* Email Recall: 1.0
* Email F1: 1.0
* Email Number: 3
* Filepath Precision: 0.8108
* Filepath Recall: 0.9091
* Filepath F1: 0.8571
* Filepath Number: 165
* Hostname Precision: 0.9231
* Hostname Recall: 1.0
* Hostname F1: 0.9600
* Hostname Number: 12
* Ipv4 Precision: 0.9167
* Ipv4 Recall: 0.9167
* Ipv4 F1: 0.9167
* Ipv4 Number: 12
* Md5 Precision: 0.875
* Md5 Recall: 0.9423
* Md5 F1: 0.9074
* Md5 Number: 52
* Sha1 Precision: 0.75
* Sha1 Recall: 0.8571
* Sha1 F1: 0.8000
* Sha1 Number: 7
* Sha256 Precision: 0.8
* Sha256 Recall: 1.0
* Sha256 F1: 0.8889
* Sha256 Number: 44
* Uri Precision: 0.0
* Uri Recall: 0.0
* Uri F1: 0.0
* Uri Number: 1
* Overall Precision: 0.8383
* Overall Recall: 0.9201
* Overall F1: 0.8773
* Overall Accuracy: 0.9816


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 #roberta #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 #roberta #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