
# RobCaamano/toxicity_weighted

This model was trained from scratch on Distilbert Base Uncased.
It achieves the following results on the evaluation set:

- Train Loss: 0.0240
- Train Precision: 0.9522
- Train Recall: 0.9190
- Epoch: 11

## Model description

Finetuned model that uses Distilbert Base Uncased to detect types of toxic text. These include: "toxic", "severe_toxic", "obscene", "threat", "insult" & "identity_hate".

## Intended uses & limitations

Intended to classify text into different types of toxicity when it is detected. Trained off a small dataset with underrepresented categories.

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 3e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32

### Training results

| Train Loss | Train Precision | Train Recall | Epoch |
|:----------:|:---------------:|:------------:|:-----:|
| 0.0440     | 0.9059          | 0.8294       | 7     |
| 0.0380     | 0.9223          | 0.8632       | 8     |
| 0.0314     | 0.9335          | 0.8838       | 9     |
| 0.0282     | 0.9437          | 0.9075       | 10    |
| 0.0240     | 0.9522          | 0.9190       | 11    |


### Framework versions

- Transformers 4.28.1
- TensorFlow 2.10.0
- Datasets 2.11.0
- Tokenizers 0.13.3

{"tags": ["generated_from_keras_callback"], "model-index": [{"name": "RobCaamano/toxicity_weighted", "results": []}]}
transformers tf distilbert text-classification generated_from_keras_callback autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #has_space #region-us 

RobCaamano/toxicity\_weighted
=============================


This model was trained from scratch on Distilbert Base Uncased.
It achieves the following results on the evaluation set:


* Train Loss: 0.0240
* Train Precision: 0.9522
* Train Recall: 0.9190
* Epoch: 11


Model description
-----------------


Finetuned model that uses Distilbert Base Uncased to detect types of toxic text. These include: "toxic", "severe\_toxic", "obscene", "threat", "insult" & "identity\_hate".


Intended uses & limitations
---------------------------


Intended to classify text into different types of toxicity when it is detected. Trained off a small dataset with underrepresented categories.


Training and evaluation data
----------------------------


More information needed


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'learning\_rate': 3e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32


### Training results



### Framework versions


* Transformers 4.28.1
* TensorFlow 2.10.0
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'learning\_rate': 3e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.28.1
* TensorFlow 2.10.0
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'learning\_rate': 3e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.28.1
* TensorFlow 2.10.0
* Datasets 2.11.0
* Tokenizers 0.13.3