Tagline

Text Tagger

Uncover the Hidden Value in Your Text Data

Efficient Tagging

Effortlessly classify textual data based on your predefined tags, saving time and improving efficiency in data processing.

Insightful Analysis

Gain valuable insights by identifying trends and key themes in your data, helping you make informed decisions.

Streamlined Operations

Enhance operational workflows by focusing on relevant data points, reducing clutter and improving response times.

Getting Started

Input some text (like a company description, customer conversation or product review) and a list of tags, and you get back a list of which tags best match your text. Tell which customer emails deal with price, what tweets are related to cryptocurrency or which product reviews you should pay attention to.

API Integration

Want to use this via API? Sign up here to get your API token and Account ID! Try out the following code examples to get this model integrated into your app quickly:

Run via CURL

curl --location --request POST 'https://predict.pyqai.com/' --header 'Authorization: <YOUR API TOKEN HERE>' --header 'Content-Type: application/json' --data-raw '{"model": "136", "version": "451", "account": "<YOUR ACCOUNT ID HERE>", "input_sequence": "Your input goes here", "candidate_labels": ["a class","another class"]}'


import requests
import json

headers = { 'Authorization': "",'Content-Type': 'application/json'}
json_input = { "model": 136, "version": 451, "account": ,"input_sequence": "Your input goes here", "candidate_labels": ["a class","another class"]}
url = "https://predict.pyqai.com"

response = requests.post(headers=headers, json = json_input, url = url )
print(json.loads(response.content))


var myHeaders = new Headers();
myHeaders.append("Authorization", "");
myHeaders.append("Content-Type", "application/json");

var raw = JSON.stringify({
  "model": 136,
  "version": 451,
  "account": ,
  "input_sequence": "Your input here",
  "candidate_labels": [
    "a category",
    "another category"
  ]
});

var requestOptions = {
  method: 'POST',
  headers: myHeaders,
  body: raw,
  redirect: 'follow'
};

fetch("https://predict.pyqai.com", requestOptions)
  .then(response => response.text())
  .then(result => console.log(result))
  .catch(error => console.log('error', error));

Reach out to us to get started with this model.

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