denounces or dehumanizes a person or a group on the basis of an identity, be it race, ethnicity, gender identity, religion, sexual orientation, ability, national origin, or another identity-group.GRAPHIC: Speech that uses visually descriptive, detailed and unpleasantly vivid imagery is considered as graphic. Such language is often made verbose so as to amplify an insult, discomfort or harm to the recipient.HARASSMENT_OR_ABUSE: Speech that imposes disruptive power dynamics between the speaker and hearer, regardless of intent, seeks to affect the psychological well-being of the recipient, or objectifies a person should be classified as Harassment.SEXUAL: Speech that indicates sexual interest, activity or arousal by using direct or indirect references to body parts or physical traits or sex is considered as toxic with toxicityType "sexual". VIOLENCE_OR_THREAT: Speech that includes threats which seek to inflict pain, injury or hostility towards a person or group.INSULT: Speech that includes demeaning, humiliating, mocking, insulting, or belittling language.PROFANITY: Speech that contains words, phrases or acronyms that are impolite, vulgar, or offensive is considered as profane.For a list of Intent labels refer to documentation [link here]Examples‚ÄãWith Hugging Face Hub Models‚ÄãGet your API Key from Hugging Face hub%pip install huggingface_hub%env HUGGINGFACEHUB_API_TOKEN="<HUGGINGFACEHUB_API_TOKEN>"# See https://huggingface.co/models?pipeline_tag=text-generation&sort=downloads for some other optionsrepo_id = "google/flan-t5-xxl"from langchain.llms import HuggingFaceHubfrom langchain.prompts import PromptTemplatefrom langchain.chains import LLMChaintemplate = """Question: {question}Answer:"""prompt = PromptTemplate(template=template, input_variables=["question"])llm = HuggingFaceHub(    repo_id=repo_id, model_kwargs={"temperature": 0.5, "max_length": 256})llm_chain = LLMChain(prompt=prompt, llm=llm)Create a configuration and initialize an Amazon Comprehend Moderation
This notebook shows how to use Amazon Comprehend to detect and handle Personally Identifiable Information (PII) and toxicity.
This notebook shows how to use Amazon Comprehend to detect and handle Personally Identifiable Information (PII) and toxicity. ->: denounces or dehumanizes a person or a group on the basis of an identity, be it race, ethnicity, gender identity, religion, sexual orientation, ability, national origin, or another identity-group.GRAPHIC: Speech that uses visually descriptive, detailed and unpleasantly vivid imagery is considered as graphic. Such language is often made verbose so as to amplify an insult, discomfort or harm to the recipient.HARASSMENT_OR_ABUSE: Speech that imposes disruptive power dynamics between the speaker and hearer, regardless of intent, seeks to affect the psychological well-being of the recipient, or objectifies a person should be classified as Harassment.SEXUAL: Speech that indicates sexual interest, activity or arousal by using direct or indirect references to body parts or physical traits or sex is considered as toxic with toxicityType "sexual". VIOLENCE_OR_THREAT: Speech that includes threats which seek to inflict pain, injury or hostility towards a person or group.INSULT: Speech that includes demeaning, humiliating, mocking, insulting, or belittling language.PROFANITY: Speech that contains words, phrases or acronyms that are impolite, vulgar, or offensive is considered as profane.For a list of Intent labels refer to documentation [link here]Examples‚ÄãWith Hugging Face Hub Models‚ÄãGet your API Key from Hugging Face hub%pip install huggingface_hub%env HUGGINGFACEHUB_API_TOKEN="<HUGGINGFACEHUB_API_TOKEN>"# See https://huggingface.co/models?pipeline_tag=text-generation&sort=downloads for some other optionsrepo_id = "google/flan-t5-xxl"from langchain.llms import HuggingFaceHubfrom langchain.prompts import PromptTemplatefrom langchain.chains import LLMChaintemplate = """Question: {question}Answer:"""prompt = PromptTemplate(template=template, input_variables=["question"])llm = HuggingFaceHub(    repo_id=repo_id, model_kwargs={"temperature": 0.5, "max_length": 256})llm_chain = LLMChain(prompt=prompt, llm=llm)Create a configuration and initialize an Amazon Comprehend Moderation