Hosted APIs

Bias analysis models on free hosted endpoints.

These models are intended for use in frontend applications. For Python data analysis and backends, you should try our PyPI package, which runs locally.

fairlyAspects (types-of bias)

Endpoint: https://t41xs75wejr14zht.us-east-1.aws.endpoints.huggingface.cloud

async function query(data) {
	const response = await fetch(
		"https://t41xs75wejr14zht.us-east-1.aws.endpoints.huggingface.cloud",
		{
			headers: { 
				"Accept" : "application/json",
				"Content-Type": "application/json" 
			},
			method: "POST",
			body: JSON.stringify(data),
		}
	);
	const result = await response.json();
	return result;
}

query({
    "inputs": "Your sentence for bias aspect classification",
    "parameters": {
        "top_k": 3,
        "function_to_apply": "sigmoid"
    }
}).then((response) => {
	console.log(JSON.stringify(response));
});

All parameters are optional

top_k: Number of classes and scores to return. Defaults to return all 11 classes and their scores.
function_to_apply: Activation function to use on the model outputs. Can be "sigmoid"(multi-label), "softmax" (mult-class), or None for raw logits. Defaults to "softmax".

import requests

API_URL = "https://t41xs75wejr14zht.us-east-1.aws.endpoints.huggingface.cloud"
headers = {
	"Accept" : "application/json",
	"Content-Type": "application/json" 
}

def query(payload):
	response = requests.post(API_URL, headers=headers, json=payload)
	return response.json()

output = query({
	"inputs": "Your sentence for bias aspect classification",
	"parameters": {
		"top_k": 3,
		"function_to_apply": "sigmoid"
	}
})

All parameters are optional

top_k: Number of classes and scores to return. Defaults to return all 11 classes and their scores.
function_to_apply: Activation function to use on the model outputs. Can be "sigmoid"(multi-label), "softmax" (mult-class), or None for raw logits. Defaults to "softmax".

curl "https://t41xs75wejr14zht.us-east-1.aws.endpoints.huggingface.cloud" \
-X POST \
-H "Accept: application/json" \-H "Content-Type: application/json" \
-d '{
    "inputs": "Your sentence for bias aspect classification",
    "parameters": {
        "top_k": 3,
        "function_to_apply": "sigmoid"
    }
}'

All parameters are optional

top_k: Number of classes and scores to return. Defaults to return all 11 classes and their scores.
function_to_apply: Activation function to use on the model outputs. Can be "sigmoid"(multi-label), "softmax" (mult-class), or None for raw logits. Defaults to "softmax".

Binary Bias Classification

Endpoint: https://e29pozks7ptdl5gw.us-east-1.aws.endpoints.huggingface.cloud

async function query(data) {
	const response = await fetch(
		"https://e29pozks7ptdl5gw.us-east-1.aws.endpoints.huggingface.cloud",
		{
			headers: { 
				"Accept" : "application/json",
				"Content-Type": "application/json" 
			},
			method: "POST",
			body: JSON.stringify(data),
		}
	);
	const result = await response.json();
	return result;
}

query({
    "inputs": "Your sentence for binary bias classification",
    "parameters": {
        "top_k": 2,
        "function_to_apply": "sigmoid"
    }
}).then((response) => {
	console.log(JSON.stringify(response));
});

All parameters are optional

top_k: Can be 1 or 2, defaults to 1, meaning it just returns the highest probable class (Biased or Neutral).
function_to_apply: Activation function to use on the model outputs. Can be "sigmoid"(multi-label), "softmax" (mult-class), or None for raw logits. Defaults to "softmax".

import requests

All parameters are optional

top_k: Can be 1 or 2, defaults to 1, meaning it just returns the highest probable class (Biased or Neutral).
function_to_apply: Activation function to use on the model outputs. Can be "sigmoid"(multi-label), "softmax" (mult-class), or None for raw logits. Defaults to "softmax".

curl "https://e29pozks7ptdl5gw.us-east-1.aws.endpoints.huggingface.cloud" \
-X POST \
-H "Accept: application/json" \-H "Content-Type: application/json" \
-d '{
    "inputs": "Your sentence for binary bias classification",
    "parameters": {
        "top_k": 2,
        "function_to_apply": "sigmoid"
    }
}'

All parameters are optional

top_k: Can be 1 or 2, defaults to 1, meaning it just returns the highest probable class (Biased or Neutral).
function_to_apply: Activation function to use on the model outputs. Can be "sigmoid"(multi-label), "softmax" (mult-class), or None for raw logits. Defaults to "softmax".

Token Classification (of generalizations, unfairness, and stereotypes)

Endpoint: https://e29pozks7ptdl5gw.us-east-1.aws.endpoints.huggingface.cloud

async function query(data) {
	const response = await fetch(
		"https://mo5fr3ll9qufbwuy.us-east-1.aws.endpoints.huggingface.cloud",
		{
			headers: { 
				"Accept" : "application/json",
				"Content-Type": "application/json" 
			},
			method: "POST",
			body: JSON.stringify(data),
		}
	);
	const result = await response.json();
	return result;
}

query({
    "inputs": "Hello world!",
    "parameters": {}
}).then((response) => {
	console.log(JSON.stringify(response));
});

*No params needed*

import requests

API_URL = "https://e29pozks7ptdl5gw.us-east-1.aws.endpoints.huggingface.cloud"
headers = {
	"Accept" : "application/json",
	"Content-Type": "application/json" 
}

def query(payload):
	response = requests.post(API_URL, headers=headers, json=payload)
	return response.json()

output = query({
	"inputs": "I like you. I love you",
	"parameters": {
		"top_k": 2,
		"function_to_apply": "sigmoid"
	}
})

*No params needed*

curl "https://e29pozks7ptdl5gw.us-east-1.aws.endpoints.huggingface.cloud" \
-X POST \
-H "Accept: application/json" \-H "Content-Type: application/json" \
-d '{
    "inputs": "I like you. I love you",
    "parameters": {
        "top_k": 2,
        "function_to_apply": "sigmoid"
    }
}'

*No params needed*

Example Response:

[
  {
    "token": "data",
    "labels": [
      "B-STEREO",
      "B-GEN"
    ]
  },
  {
    "token": "scientists",
    "labels": [
      "B-GEN",
      "I-GEN"
    ]
  },
  {
    "token": "are",
    "labels": [
      "I-STEREO"
    ]
  },
  {
    "token": "so",
    "labels": [
      "I-STEREO"
    ]
  },
  {
    "token": "smart",
    "labels": [
      "I-STEREO"
    ]
  }
]

PreviousMultimodalAnalyzer Pipeline NextSequence Classification

Last updated 19 days ago

async function query(data) { const response = await fetch( "https://t41xs75wejr14zht.us-east-1.aws.endpoints.huggingface.cloud", { headers: { "Accept" : "application/json", "Content-Type": "application/json" }, method: "POST", body: JSON.stringify(data), } ); const result = await response.json(); return result; } query({ "inputs": "Your sentence for bias aspect classification", "parameters": { "top_k": 3, "function_to_apply": "sigmoid" } }).then((response) => { console.log(JSON.stringify(response)); });

import requests API_URL = "https://t41xs75wejr14zht.us-east-1.aws.endpoints.huggingface.cloud" headers = { "Accept" : "application/json", "Content-Type": "application/json" } def query(payload): response = requests.post(API_URL, headers=headers, json=payload) return response.json() output = query({ "inputs": "Your sentence for bias aspect classification", "parameters": { "top_k": 3, "function_to_apply": "sigmoid" } })

curl "https://t41xs75wejr14zht.us-east-1.aws.endpoints.huggingface.cloud" \ -X POST \ -H "Accept: application/json" \-H "Content-Type: application/json" \ -d '{ "inputs": "Your sentence for bias aspect classification", "parameters": { "top_k": 3, "function_to_apply": "sigmoid" } }'

async function query(data) { const response = await fetch( "https://e29pozks7ptdl5gw.us-east-1.aws.endpoints.huggingface.cloud", { headers: { "Accept" : "application/json", "Content-Type": "application/json" }, method: "POST", body: JSON.stringify(data), } ); const result = await response.json(); return result; } query({ "inputs": "Your sentence for binary bias classification", "parameters": { "top_k": 2, "function_to_apply": "sigmoid" } }).then((response) => { console.log(JSON.stringify(response)); });

curl "https://e29pozks7ptdl5gw.us-east-1.aws.endpoints.huggingface.cloud" \ -X POST \ -H "Accept: application/json" \-H "Content-Type: application/json" \ -d '{ "inputs": "Your sentence for binary bias classification", "parameters": { "top_k": 2, "function_to_apply": "sigmoid" } }'

async function query(data) { const response = await fetch( "https://mo5fr3ll9qufbwuy.us-east-1.aws.endpoints.huggingface.cloud", { headers: { "Accept" : "application/json", "Content-Type": "application/json" }, method: "POST", body: JSON.stringify(data), } ); const result = await response.json(); return result; } query({ "inputs": "Hello world!", "parameters": {} }).then((response) => { console.log(JSON.stringify(response)); });

import requests API_URL = "https://e29pozks7ptdl5gw.us-east-1.aws.endpoints.huggingface.cloud" headers = { "Accept" : "application/json", "Content-Type": "application/json" } def query(payload): response = requests.post(API_URL, headers=headers, json=payload) return response.json() output = query({ "inputs": "I like you. I love you", "parameters": { "top_k": 2, "function_to_apply": "sigmoid" } })

curl "https://e29pozks7ptdl5gw.us-east-1.aws.endpoints.huggingface.cloud" \ -X POST \ -H "Accept: application/json" \-H "Content-Type: application/json" \ -d '{ "inputs": "I like you. I love you", "parameters": { "top_k": 2, "function_to_apply": "sigmoid" } }'

[ { "token": "data", "labels": [ "B-STEREO", "B-GEN" ] }, { "token": "scientists", "labels": [ "B-GEN", "I-GEN" ] }, { "token": "are", "labels": [ "I-STEREO" ] }, { "token": "so", "labels": [ "I-STEREO" ] }, { "token": "smart", "labels": [ "I-STEREO" ] } ]