
#distilbert-base-uncased

This model is based on the pre-trained model [distilbert-base-uncased] and was fine-tuned on a dataset of tweets from Kaggle's Toxic Comment Classification Challenge

### Inputs

The model has been trained on the toxicity of tweets ranging from toxic, severe toxic, obscene, threat, insult, hate speech

### Outputs

The model predicts 6 signals of toxicity:

Toxic
Severe Toxic
Obscene
Threat
Insult
Hate Speech

A value between 0 and 1 is predicted for each signal.

### Intended uses & limitations 

The model was created to be used as a toxicity detector of tweets based on the six categories. Other forms of toxicity from tweets may not be calculated with this model.


### How to use

The model can be used directly with a text-classification pipeline:

```python
>>> from transformers import pipeline
>>> text = "Your vandalism to the Matt Shirvington article has been reverted.  Please don't do it again, or you will be banned."
>>> pipe = pipeline("text-classification", model="dk3156/toxic_tweets_model")
>>> pipe(text, return_all_scores=True)
[[{'label0': 'score': 0.02},
  {'label1': 'score': 0.0},
  {'label2':  'score': 0.0},
{'label3':  'score': 0.0},
{'label4':  'score': 0.0},
{'label5':  'score': 0.0}]]
```

### Training procedure
The pre-trained model was fine-tuned for sequence classification using the following hyperparameters, which were selected from a validation set:

* Batch size = 16
* Learning rate = 5e-5
* Epochs = 1


The optimizer used was AdamW.
{"language": "en"}
transformers pytorch distilbert text-classification en autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #distilbert #text-classification #en #autotrain_compatible #endpoints_compatible #has_space #region-us 


#distilbert-base-uncased

This model is based on the pre-trained model [distilbert-base-uncased] and was fine-tuned on a dataset of tweets from Kaggle's Toxic Comment Classification Challenge

### Inputs

The model has been trained on the toxicity of tweets ranging from toxic, severe toxic, obscene, threat, insult, hate speech

### Outputs

The model predicts 6 signals of toxicity:

Toxic
Severe Toxic
Obscene
Threat
Insult
Hate Speech

A value between 0 and 1 is predicted for each signal.

### Intended uses & limitations 

The model was created to be used as a toxicity detector of tweets based on the six categories. Other forms of toxicity from tweets may not be calculated with this model.


### How to use

The model can be used directly with a text-classification pipeline:



### Training procedure
The pre-trained model was fine-tuned for sequence classification using the following hyperparameters, which were selected from a validation set:

* Batch size = 16
* Learning rate = 5e-5
* Epochs = 1


The optimizer used was AdamW.
### Inputs

The model has been trained on the toxicity of tweets ranging from toxic, severe toxic, obscene, threat, insult, hate speech ### Outputs

The model predicts 6 signals of toxicity:

Toxic
Severe Toxic
Obscene
Threat
Insult
Hate Speech

A value between 0 and 1 is predicted for each signal. ### Intended uses & limitations 

The model was created to be used as a toxicity detector of tweets based on the six categories. Other forms of toxicity from tweets may not be calculated with this model. ### How to use

The model can be used directly with a text-classification pipeline: ### Training procedure
The pre-trained model was fine-tuned for sequence classification using the following hyperparameters, which were selected from a validation set:

* Batch size = 16
* Learning rate = 5e-5
* Epochs = 1


The optimizer used was AdamW.
TAGS
#transformers #pytorch #distilbert #text-classification #en #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### Inputs

The model has been trained on the toxicity of tweets ranging from toxic, severe toxic, obscene, threat, insult, hate speech ### Outputs

The model predicts 6 signals of toxicity:

Toxic
Severe Toxic
Obscene
Threat
Insult
Hate Speech

A value between 0 and 1 is predicted for each signal. ### Intended uses & limitations 

The model was created to be used as a toxicity detector of tweets based on the six categories. Other forms of toxicity from tweets may not be calculated with this model. ### How to use

The model can be used directly with a text-classification pipeline: ### Training procedure
The pre-trained model was fine-tuned for sequence classification using the following hyperparameters, which were selected from a validation set:

* Batch size = 16
* Learning rate = 5e-5
* Epochs = 1


The optimizer used was AdamW.
TAGS
#transformers #pytorch #distilbert #text-classification #en #autotrain_compatible #endpoints_compatible #has_space #region-us 
### Inputs

The model has been trained on the toxicity of tweets ranging from toxic, severe toxic, obscene, threat, insult, hate speech### Outputs

The model predicts 6 signals of toxicity:

Toxic
Severe Toxic
Obscene
Threat
Insult
Hate Speech

A value between 0 and 1 is predicted for each signal.### Intended uses & limitations 

The model was created to be used as a toxicity detector of tweets based on the six categories. Other forms of toxicity from tweets may not be calculated with this model.### How to use

The model can be used directly with a text-classification pipeline:### Training procedure
The pre-trained model was fine-tuned for sequence classification using the following hyperparameters, which were selected from a validation set:

* Batch size = 16
* Learning rate = 5e-5
* Epochs = 1


The optimizer used was AdamW.