deberta-v3-small: Responses
curl --request POST \
--url https://api.zerogpu.ai/v1/responses \
--header 'Content-Type: application/json' \
--header 'x-api-key: <api-key>' \
--data '
{
"input": "Apple is expected to unveil its next-generation M5 chip at WWDC this June, promising a 40% boost in GPU performance and a new dedicated AI core for on-device machine learning tasks.",
"model": "deberta-v3-small"
}
'import requests
url = "https://api.zerogpu.ai/v1/responses"
payload = {
"input": "Apple is expected to unveil its next-generation M5 chip at WWDC this June, promising a 40% boost in GPU performance and a new dedicated AI core for on-device machine learning tasks.",
"model": "deberta-v3-small"
}
headers = {
"x-api-key": "<api-key>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {'x-api-key': '<api-key>', 'Content-Type': 'application/json'},
body: JSON.stringify({
input: 'Apple is expected to unveil its next-generation M5 chip at WWDC this June, promising a 40% boost in GPU performance and a new dedicated AI core for on-device machine learning tasks.',
model: 'deberta-v3-small'
})
};
fetch('https://api.zerogpu.ai/v1/responses', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));falsepackage main
import (
"fmt"
"strings"
"net/http"
"io"
)
func main() {
url := "https://api.zerogpu.ai/v1/responses"
payload := strings.NewReader("{\n \"input\": \"Apple is expected to unveil its next-generation M5 chip at WWDC this June, promising a 40% boost in GPU performance and a new dedicated AI core for on-device machine learning tasks.\",\n \"model\": \"deberta-v3-small\"\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("x-api-key", "<api-key>")
req.Header.Add("Content-Type", "application/json")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(string(body))
}require 'uri'
require 'net/http'
url = URI("https://api.zerogpu.ai/v1/responses")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["x-api-key"] = '<api-key>'
request["Content-Type"] = 'application/json'
request.body = "{\n \"input\": \"Apple is expected to unveil its next-generation M5 chip at WWDC this June, promising a 40% boost in GPU performance and a new dedicated AI core for on-device machine learning tasks.\",\n \"model\": \"deberta-v3-small\"\n}"
response = http.request(request)
puts response.read_body{
"technology": 0.971784,
"politics": 0.011858,
"sports": 0.01175,
"finance": 0.004608
}{}By model
deberta-v3-small
Model details for deberta-v3-small.
POST
/
responses
deberta-v3-small: Responses
curl --request POST \
--url https://api.zerogpu.ai/v1/responses \
--header 'Content-Type: application/json' \
--header 'x-api-key: <api-key>' \
--data '
{
"input": "Apple is expected to unveil its next-generation M5 chip at WWDC this June, promising a 40% boost in GPU performance and a new dedicated AI core for on-device machine learning tasks.",
"model": "deberta-v3-small"
}
'import requests
url = "https://api.zerogpu.ai/v1/responses"
payload = {
"input": "Apple is expected to unveil its next-generation M5 chip at WWDC this June, promising a 40% boost in GPU performance and a new dedicated AI core for on-device machine learning tasks.",
"model": "deberta-v3-small"
}
headers = {
"x-api-key": "<api-key>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {'x-api-key': '<api-key>', 'Content-Type': 'application/json'},
body: JSON.stringify({
input: 'Apple is expected to unveil its next-generation M5 chip at WWDC this June, promising a 40% boost in GPU performance and a new dedicated AI core for on-device machine learning tasks.',
model: 'deberta-v3-small'
})
};
fetch('https://api.zerogpu.ai/v1/responses', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));falsepackage main
import (
"fmt"
"strings"
"net/http"
"io"
)
func main() {
url := "https://api.zerogpu.ai/v1/responses"
payload := strings.NewReader("{\n \"input\": \"Apple is expected to unveil its next-generation M5 chip at WWDC this June, promising a 40% boost in GPU performance and a new dedicated AI core for on-device machine learning tasks.\",\n \"model\": \"deberta-v3-small\"\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("x-api-key", "<api-key>")
req.Header.Add("Content-Type", "application/json")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(string(body))
}require 'uri'
require 'net/http'
url = URI("https://api.zerogpu.ai/v1/responses")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["x-api-key"] = '<api-key>'
request["Content-Type"] = 'application/json'
request.body = "{\n \"input\": \"Apple is expected to unveil its next-generation M5 chip at WWDC this June, promising a 40% boost in GPU performance and a new dedicated AI core for on-device machine learning tasks.\",\n \"model\": \"deberta-v3-small\"\n}"
response = http.request(request)
puts response.read_body{
"technology": 0.971784,
"politics": 0.011858,
"sports": 0.01175,
"finance": 0.004608
}{}Microsoft’s DeBERTa-v3-small is a fast, lightweight zero-shot text classifier for high-volume routing, filtering, and tagging. Hand it any text alongside your own candidate labels and it returns a confidence score for each. No training data and no fixed taxonomy, just the categories you define at request time. Because it scores every label rather than forcing one choice, you get soft probabilities to threshold, rank, or route on however your pipeline needs. Purpose-built and CPU-optimized, it runs faster and cheaper than asking a general-purpose frontier model to classify.References: Model docs • Terms • Privacy
Authorizations
Headers
Optional project identifier. Scopes the request to a specific project when provided.
Body
application/json
Model identifier (fixed for this playground). Use request examples to change use cases.
Allowed value:
"deberta-v3-small"Example:
"deberta-v3-small"
Multi-line text or document content to send to the model.
Required string length:
1 - 131072Candidate categories for zero-shot classification, passed as a comma-separated array (e.g. [sports, finance, politics]). A maximum of 10 categories is supported; response time increases with the number of categories provided.
Example:
"[sports, finance, politics]"
Response
Success
The response is of type object.