curl -X POST "https://api.fastino.com/run" \
-H "x-api-key: <PLACEHOLDER_FASTINO_API_KEY>" \
-H "Content-Type: application/json" \
-d '{
"model_id": "fastino-pii",
"input": [
{
"text": "Jessica L. Martinez, a 34-year-old Hispanic woman, graduated from Stanford University in 2013 with a degree in Computer Science. She currently works as a senior software engineer at a tech company in Palo Alto, earning an annual income of $145,000. She is married and lives in San Francisco on an H-1B visa. Jessica speaks both English and Spanish and is active on social media under the handle @jess_code. Her email address is jessica.martinez@example.com, and her phone number is (415) 555-0198. She received a merit-based scholarship and financial aid during her undergraduate studies. Her credit score is 790. She identifies as bisexual and practices Buddhism. Her nationality is Mexican, and her net worth is estimated at $300,000.",
"parameters": {
"entity_types": [
"first name",
"surname",
"email",
"age",
"ethnic background",
"gender",
"university",
"date",
"degree",
"job",
"city",
"visa type",
"annual pay",
"language",
"phone number",
"net worth",
"religion",
"sexuality",
"marital status",
"study",
"social handle",
"credit score",
"scolarship",
"nationality",
"middle name"
],
"threshold": 0.3,
"multi_label": true,
"flat_ner": false
}
}
]
}'
import requests
import os
url = "https://api.fastino.com/run"
api_key = os.getenv("FASTINO_API_KEY")
headers = {
"x-api-key": api_key,
"Content-Type": "application/json"
}
data = {
model_id: "fastino-pii",
input: [
{
"text": "Jessica L. Martinez, a 34-year-old Hispanic woman, graduated from Stanford University in 2013 with a degree in Computer Science. She currently works as a senior software engineer at a tech company in Palo Alto, earning an annual income of $145,000. She is married and lives in San Francisco on an H-1B visa. Jessica speaks both English and Spanish and is active on social media under the handle @jess_code. Her email address is jessica.martinez@example.com, and her phone number is (415) 555-0198. She received a merit-based scholarship and financial aid during her undergraduate studies. Her credit score is 790. She identifies as bisexual and practices Buddhism. Her nationality is Mexican, and her net worth is estimated at $300,000.",
"parameters": {
"entity_types": [
"first name",
"surname",
"email",
"age",
"ethnic background",
"gender",
"university",
"date",
"degree",
"job",
"city",
"visa type",
"annual pay",
"language",
"phone number",
"net worth",
"religion",
"sexuality",
"marital status",
"study",
"social handle",
"credit score",
"scolarship",
"nationality",
"middle name"
],
"threshold": 0.3,
"multi_label": true,
"flat_ner": false
}
}
]
}
response = requests.post(url, headers=headers, json=data)
# Print the response
print(response.status_code, response.json())
const url = "https://api.fastino.com/run";
const apiKey = process.env.FASTINO_API_KEY;
const headers = {
"x-api-key": apiKey,
"Content-Type": "application/json"
};
const data = {
model_id: "fastino-pii",
input: [
{
"text": "Jessica L. Martinez, a 34-year-old Hispanic woman, graduated from Stanford University in 2013 with a degree in Computer Science. She currently works as a senior software engineer at a tech company in Palo Alto, earning an annual income of $145,000. She is married and lives in San Francisco on an H-1B visa. Jessica speaks both English and Spanish and is active on social media under the handle @jess_code. Her email address is jessica.martinez@example.com, and her phone number is (415) 555-0198. She received a merit-based scholarship and financial aid during her undergraduate studies. Her credit score is 790. She identifies as bisexual and practices Buddhism. Her nationality is Mexican, and her net worth is estimated at $300,000.",
"parameters": {
"entity_types": [
"first name",
"surname",
"email",
"age",
"ethnic background",
"gender",
"university",
"date",
"degree",
"job",
"city",
"visa type",
"annual pay",
"language",
"phone number",
"net worth",
"religion",
"sexuality",
"marital status",
"study",
"social handle",
"credit score",
"scolarship",
"nationality",
"middle name"
],
"threshold": 0.3,
"multi_label": true,
"flat_ner": false
}
}
]
}
const runInference = async () => {
try {
const res = await fetch(url, {
method: "POST",
headers,
body: JSON.stringify(data)
});
const result = await res.json();
console.log("Response:", result);
} catch (error) {
console.error("Error:", error);
}
};
