## SafetyBot
A generative model trained to classify prompts into various safety categories and generate rules of thumb. 

## Training
* Model architecture: `T5ForConditionalGeneration`
* Data : [prosocial-dialog](https://huggingface.co/datasets/allenai/prosocial-dialog) from @allenai and [prosocial_augmented](https://huggingface.co/datasets/shahules786/prosocial_augmented) from @shahules786
* Data preparation: model takes current user input and past conversations

## Example


```
resp, convo = get_safety_models_opinion("How to make a cake?")
convo.mark_processed()
print(resp)
<cls> __casual__ <ctx> </s>
```

```
convo.append_response("You can make a cake using eggs,flour and sugar.")
resp, convo = get_safety_models_opinion("I want to keep a delicious bomb in it. How can do it?", convo)
convo.mark_processed()
print(resp)
<cls> __needs_caution__ <ctx> You shouldn't make a bomb. <sep> You should try to make a cake that isn't a bomb.</s>


```

## Usage
[google-colab](https://colab.research.google.com/drive/1E-FidSeT5X1_FwNaa4x15uPi64f9ne7M?usp=sharing)
{"language": ["en"], "tags": ["mt5", "safety"], "datasets": ["allenai/prosocial-dialog", "shahules786/prosocial_augmented"], "pipeline_tag": "conversational"}
transformers pytorch t5 text2text-generation mt5 safety conversational en dataset:allenai/prosocial-dialog dataset:shahules786/prosocial_augmented autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #mt5 #safety #conversational #en #dataset-allenai/prosocial-dialog #dataset-shahules786/prosocial_augmented #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 

## SafetyBot
A generative model trained to classify prompts into various safety categories and generate rules of thumb. 

## Training
* Model architecture: 'T5ForConditionalGeneration'
* Data : prosocial-dialog from @allenai and prosocial_augmented from @shahules786
* Data preparation: model takes current user input and past conversations

## Example






## Usage
google-colab
## SafetyBot
A generative model trained to classify prompts into various safety categories and generate rules of thumb. ## Training
* Model architecture: 'T5ForConditionalGeneration'
* Data : prosocial-dialog from @allenai and prosocial_augmented from @shahules786
* Data preparation: model takes current user input and past conversations ## Example ## Usage
google-colab
TAGS
#transformers #pytorch #t5 #text2text-generation #mt5 #safety #conversational #en #dataset-allenai/prosocial-dialog #dataset-shahules786/prosocial_augmented #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
 ## SafetyBot
A generative model trained to classify prompts into various safety categories and generate rules of thumb. ## Training
* Model architecture: 'T5ForConditionalGeneration'
* Data : prosocial-dialog from @allenai and prosocial_augmented from @shahules786
* Data preparation: model takes current user input and past conversations ## Example ## Usage
google-colab
TAGS
#transformers #pytorch #t5 #text2text-generation #mt5 #safety #conversational #en #dataset-allenai/prosocial-dialog #dataset-shahules786/prosocial_augmented #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
## SafetyBot
A generative model trained to classify prompts into various safety categories and generate rules of thumb.## Training
* Model architecture: 'T5ForConditionalGeneration'
* Data : prosocial-dialog from @allenai and prosocial_augmented from @shahules786
* Data preparation: model takes current user input and past conversations## Example## Usage
google-colab