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# training

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the cynthiachan/FeedRef_10pct dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1291
- Attackid Precision: 1.0
- Attackid Recall: 1.0
- Attackid F1: 1.0
- Attackid Number: 6
- Cve Precision: 0.8333
- Cve Recall: 0.9091
- Cve F1: 0.8696
- Cve Number: 11
- Defenderthreat Precision: 0.0
- Defenderthreat Recall: 0.0
- Defenderthreat F1: 0.0
- Defenderthreat Number: 2
- Domain Precision: 0.7826
- Domain Recall: 0.7826
- Domain F1: 0.7826
- Domain Number: 23
- Email Precision: 0.6667
- Email Recall: 0.6667
- Email F1: 0.6667
- Email Number: 3
- Filepath Precision: 0.6766
- Filepath Recall: 0.8242
- Filepath F1: 0.7432
- Filepath Number: 165
- Hostname Precision: 1.0
- Hostname Recall: 0.9167
- Hostname F1: 0.9565
- Hostname Number: 12
- Ipv4 Precision: 0.8333
- Ipv4 Recall: 0.8333
- Ipv4 F1: 0.8333
- Ipv4 Number: 12
- Md5 Precision: 0.7246
- Md5 Recall: 0.9615
- Md5 F1: 0.8264
- Md5 Number: 52
- Sha1 Precision: 0.0667
- Sha1 Recall: 0.1429
- Sha1 F1: 0.0909
- Sha1 Number: 7
- Sha256 Precision: 0.6780
- Sha256 Recall: 0.9091
- Sha256 F1: 0.7767
- Sha256 Number: 44
- Uri Precision: 0.0
- Uri Recall: 0.0
- Uri F1: 0.0
- Uri Number: 1
- Overall Precision: 0.6910
- Overall Recall: 0.8402
- Overall F1: 0.7583
- Overall Accuracy: 0.9725

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------------:|:---------------:|:-----------:|:---------------:|:-------------:|:----------:|:------:|:----------:|:------------------------:|:---------------------:|:-----------------:|:---------------------:|:----------------:|:-------------:|:---------:|:-------------:|:---------------:|:------------:|:--------:|:------------:|:------------------:|:---------------:|:-----------:|:---------------:|:------------------:|:---------------:|:-----------:|:---------------:|:--------------:|:-----------:|:-------:|:-----------:|:-------------:|:----------:|:------:|:----------:|:--------------:|:-----------:|:-------:|:-----------:|:----------------:|:-------------:|:---------:|:-------------:|:-------------:|:----------:|:------:|:----------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.3943        | 0.37  | 500  | 0.2881          | 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            | 0.1138             | 0.2             | 0.1451      | 165             | 0.0692             | 0.9167          | 0.1287      | 12              | 0.4706         | 0.6667      | 0.5517  | 12          | 0.75          | 0.9231     | 0.8276 | 52         | 0.0            | 0.0         | 0.0     | 7           | 0.5694           | 0.9318        | 0.7069    | 44            | 0.0           | 0.0        | 0.0    | 1          | 0.2342            | 0.4172         | 0.3        | 0.9360           |
| 0.1987        | 0.75  | 1000 | 0.1722          | 0.5                | 1.0             | 0.6667      | 6               | 1.0           | 1.0        | 1.0    | 11         | 0.0                      | 0.0                   | 0.0               | 2                     | 0.0              | 0.0           | 0.0       | 23            | 0.0             | 0.0          | 0.0      | 3            | 0.4779             | 0.6545          | 0.5524      | 165             | 0.25               | 0.6667          | 0.3636      | 12              | 0.6923         | 0.75        | 0.7200  | 12          | 0.6364        | 0.9423     | 0.7597 | 52         | 0.0            | 0.0         | 0.0     | 7           | 0.6545           | 0.8182        | 0.7273    | 44            | 0.0           | 0.0        | 0.0    | 1          | 0.5136            | 0.6716         | 0.5821     | 0.9529           |
| 0.1595        | 1.12  | 1500 | 0.1346          | 0.8571             | 1.0             | 0.9231      | 6               | 1.0           | 1.0        | 1.0    | 11         | 0.0                      | 0.0                   | 0.0               | 2                     | 0.4286           | 0.5217        | 0.4706    | 23            | 0.0             | 0.0          | 0.0      | 3            | 0.5797             | 0.7273          | 0.6452      | 165             | 0.44               | 0.9167          | 0.5946      | 12              | 0.3929         | 0.9167      | 0.55    | 12          | 0.6364        | 0.9423     | 0.7597 | 52         | 0.0            | 0.0         | 0.0     | 7           | 0.78             | 0.8864        | 0.8298    | 44            | 0.0           | 0.0        | 0.0    | 1          | 0.5768            | 0.7663         | 0.6582     | 0.9658           |
| 0.118         | 1.5   | 2000 | 0.1436          | 1.0                | 1.0             | 1.0         | 6               | 1.0           | 1.0        | 1.0    | 11         | 0.0                      | 0.0                   | 0.0               | 2                     | 0.6087           | 0.6087        | 0.6087    | 23            | 0.0             | 0.0          | 0.0      | 3            | 0.6101             | 0.8061          | 0.6945      | 165             | 0.9091             | 0.8333          | 0.8696      | 12              | 0.7273         | 0.6667      | 0.6957  | 12          | 0.7869        | 0.9231     | 0.8496 | 52         | 0.2143         | 0.4286      | 0.2857  | 7           | 0.7407           | 0.9091        | 0.8163    | 44            | 0.0           | 0.0        | 0.0    | 1          | 0.6675            | 0.8077         | 0.7309     | 0.9686           |
| 0.1198        | 1.87  | 2500 | 0.1385          | 1.0                | 1.0             | 1.0         | 6               | 0.7692        | 0.9091     | 0.8333 | 11         | 0.0                      | 0.0                   | 0.0               | 2                     | 0.85             | 0.7391        | 0.7907    | 23            | 0.0             | 0.0          | 0.0      | 3            | 0.6390             | 0.7939          | 0.7081      | 165             | 1.0                | 0.8333          | 0.9091      | 12              | 0.5333         | 0.6667      | 0.5926  | 12          | 0.7778        | 0.9423     | 0.8522 | 52         | 0.3333         | 0.5714      | 0.4211  | 7           | 0.8571           | 0.9545        | 0.9032    | 44            | 0.0           | 0.0        | 0.0    | 1          | 0.6995            | 0.8195         | 0.7548     | 0.9687           |
| 0.0742        | 2.25  | 3000 | 0.1291          | 1.0                | 1.0             | 1.0         | 6               | 0.8333        | 0.9091     | 0.8696 | 11         | 0.0                      | 0.0                   | 0.0               | 2                     | 0.7826           | 0.7826        | 0.7826    | 23            | 0.6667          | 0.6667       | 0.6667   | 3            | 0.6766             | 0.8242          | 0.7432      | 165             | 1.0                | 0.9167          | 0.9565      | 12              | 0.8333         | 0.8333      | 0.8333  | 12          | 0.7246        | 0.9615     | 0.8264 | 52         | 0.0667         | 0.1429      | 0.0909  | 7           | 0.6780           | 0.9091        | 0.7767    | 44            | 0.0           | 0.0        | 0.0    | 1          | 0.6910            | 0.8402         | 0.7583     | 0.9725           |
| 0.0687        | 2.62  | 3500 | 0.1385          | 1.0                | 1.0             | 1.0         | 6               | 1.0           | 1.0        | 1.0    | 11         | 0.0                      | 0.0                   | 0.0               | 2                     | 0.8077           | 0.9130        | 0.8571    | 23            | 1.0             | 1.0          | 1.0      | 3            | 0.7746             | 0.8121          | 0.7929      | 165             | 0.7333             | 0.9167          | 0.8148      | 12              | 0.7143         | 0.8333      | 0.7692  | 12          | 0.96          | 0.9231     | 0.9412 | 52         | 0.4444         | 0.5714      | 0.5     | 7           | 0.8113           | 0.9773        | 0.8866    | 44            | 0.0           | 0.0        | 0.0    | 1          | 0.8083            | 0.8609         | 0.8338     | 0.9737           |
| 0.0652        | 3.0   | 4000 | 0.1299          | 1.0                | 1.0             | 1.0         | 6               | 1.0           | 1.0        | 1.0    | 11         | 0.0                      | 0.0                   | 0.0               | 2                     | 0.8077           | 0.9130        | 0.8571    | 23            | 1.0             | 1.0          | 1.0      | 3            | 0.7553             | 0.8606          | 0.8045      | 165             | 0.8462             | 0.9167          | 0.8800      | 12              | 0.7143         | 0.8333      | 0.7692  | 12          | 0.8571        | 0.9231     | 0.8889 | 52         | 0.75           | 0.8571      | 0.8000  | 7           | 0.8723           | 0.9318        | 0.9011    | 44            | 0.0           | 0.0        | 0.0    | 1          | 0.8038            | 0.8846         | 0.8423     | 0.9772           |


### 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 bert token-classification generated_from_trainer dataset:cynthiachan/FeedRef_10pct license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #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 bert-base-cased on the cynthiachan/FeedRef\_10pct dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1291
* Attackid Precision: 1.0
* Attackid Recall: 1.0
* Attackid F1: 1.0
* Attackid Number: 6
* Cve Precision: 0.8333
* Cve Recall: 0.9091
* Cve F1: 0.8696
* Cve Number: 11
* Defenderthreat Precision: 0.0
* Defenderthreat Recall: 0.0
* Defenderthreat F1: 0.0
* Defenderthreat Number: 2
* Domain Precision: 0.7826
* Domain Recall: 0.7826
* Domain F1: 0.7826
* Domain Number: 23
* Email Precision: 0.6667
* Email Recall: 0.6667
* Email F1: 0.6667
* Email Number: 3
* Filepath Precision: 0.6766
* Filepath Recall: 0.8242
* Filepath F1: 0.7432
* Filepath Number: 165
* Hostname Precision: 1.0
* Hostname Recall: 0.9167
* Hostname F1: 0.9565
* Hostname Number: 12
* Ipv4 Precision: 0.8333
* Ipv4 Recall: 0.8333
* Ipv4 F1: 0.8333
* Ipv4 Number: 12
* Md5 Precision: 0.7246
* Md5 Recall: 0.9615
* Md5 F1: 0.8264
* Md5 Number: 52
* Sha1 Precision: 0.0667
* Sha1 Recall: 0.1429
* Sha1 F1: 0.0909
* Sha1 Number: 7
* Sha256 Precision: 0.6780
* Sha256 Recall: 0.9091
* Sha256 F1: 0.7767
* Sha256 Number: 44
* Uri Precision: 0.0
* Uri Recall: 0.0
* Uri F1: 0.0
* Uri Number: 1
* Overall Precision: 0.6910
* Overall Recall: 0.8402
* Overall F1: 0.7583
* Overall Accuracy: 0.9725


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 #bert #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 #bert #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