71trust / 100

Assistant Sector Ml Sentiment Log Loss

by Sentiment - Multiclass log loss in Developer tools

x402 APIPassing, checked 3 h ago

Sentiment - Multiclass log loss: Multiclass log loss for Sentiment. Up to 200 supplied labelled text records. Local dataset tools, evaluation and small naive-Bayes baselines; no hosted model or foundation-model training. One of 25 profiles or calculations across 20 shared operations in this sector. 0.01 USDC includes one bounded multiclass log loss deliverable.

POST

https://market.datapackvibe.com/x402/assistant-sector-ml-sentiment-log-loss

Price: $0.01; per call in USDC
Uptime: 77.4%; last 30 days
Response time: 90 ms; 2.9 s slowest 5%
Last check: Passed; 3 h ago, next any minute now

Run a free test call How to call it

Reliability

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Status and outage history · Atom feed · Watch with other listings

How to call it

POST https://market.datapackvibe.com/x402/assistant-sector-ml-sentiment-log-loss

Each call costs $0.01 in USDC on Base, paid with the request over x402.

Run a free test callOne unpaid request, the way an agent sends it. You see the payment challenge it gets back. Nothing is charged.

Example request

From the listing.

POST https://market.datapackvibe.com/x402/assistant-sector-ml-sentiment-log-loss
content-type: application/json

{
  "records": [
    {
      "actual": "positive",
      "id": "example-0-0",
      "predicted": "positive",
      "probabilities": [
        0.8,
        0.1,
        0.1
      ],
      "text": "positive sample document number 0 for this supplied dataset."
    },
    {
      "actual": "positive",
      "id": "example-0-1",
      "predicted": "positive",
      "probabilities": [
        0.8,
        0.1,
        0.1
      ],
      "text": "positive sample document number 1 for this supplied dataset."
    },
    {
      "actual": "positive",
      "id": "example-0-2",
      "predicted": "positive",
      "probabilities": [
        0.8,
        0.1,
        0.1
      ],
      "text": "positive sample document number 2 for this supplied dataset."
    },
    {
      "actual": "neutral",
      "id": "example-1-0",
      "predicted": "neutral",
      "probabilities": [
        0.1,
        0.8,
        0.1
      ],
      "text": "neutral sample document number 0 for this supplied dataset."
    },
    {
      "actual": "neutral",
      "id": "example-1-1",
      "predicted": "neutral",
      "probabilities": [
        0.1,
        0.8,
        0.1
      ],
      "text": "neutral sample document number 1 for this supplied dataset."
    },
    {
      "actual": "neutral",
      "id": "example-1-2",
      "predicted": "neutral",
      "probabilities": [
        0.1,
        0.8,
        0.1
      ],
      "text": "neutral sample document number 2 for this supplied dataset."
    },
    {
      "actual": "negative",
      "id": "example-2-0",
      "predicted": "negative",
      "probabilities": [
        0.1,
        0.1,
        0.8
      ],
      "text": "negative sample document number 0 for this supplied dataset."
    },
    {
      "actual": "negative",
      "id": "example-2-1",
      "predicted": "negative",
      "probabilities": [
        0.1,
        0.1,
        0.8
      ],
      "text": "negative sample document number 1 for this supplied dataset."
    },
    {
      "actual": "negative",
      "id": "example-2-2",
      "predicted": "negative",
      "probabilities": [
        0.1,
        0.1,
        0.8
      ],
      "text": "negative sample document number 2 for this supplied dataset."
    }
  ]
}

Parameters

Parameter	Type	Required	Description
`records`	array of object	Yes	Default: [{"actual":"positive","id":"example-0-0","predicted":"positive","probabilities"….

Example response

The provider's example. We can't check it without paying.

{
  "files": [],
  "order_id": "ord_example",
  "result": {
    "completed": true,
    "workflow": "sector-ml-sentiment-log-loss"
  },
  "sku": "assistant-sector-ml-sentiment-log-loss",
  "status": "delivered"
}

Call it from code

The curl command shows the payment challenge. The JavaScript and Python versions pay and call, and never pay more than $0.01.

curl: see the payment challenge

# bash or zsh: see the payment challenge. Nothing is charged.
curl -sS -g -i -X POST 'https://market.datapackvibe.com/x402/assistant-sector-ml-sentiment-log-loss' \
  -H 'content-type: application/json' \
  --data-raw '{"records":[{"actual":"positive","id":"example-0-0","predicted":"positive","probabilities":[0.8,0.1,0.1],"text":"positive sample document number 0 for this supplied dataset."},{"actual":"positive","id":"example-0-1","predicted":"positive","probabilities":[0.8,0.1,0.1],"text":"positive sample document number 1 for this supplied dataset."},{"actual":"positive","id":"example-0-2","predicted":"positive","probabilities":[0.8,0.1,0.1],"text":"positive sample document number 2 for this supplied dataset."},{"actual":"neutral","id":"example-1-0","predicted":"neutral","probabilities":[0.1,0.8,0.1],"text":"neutral sample document number 0 for this supplied dataset."},{"actual":"neutral","id":"example-1-1","predicted":"neutral","probabilities":[0.1,0.8,0.1],"text":"neutral sample document number 1 for this supplied dataset."},{"actual":"neutral","id":"example-1-2","predicted":"neutral","probabilities":[0.1,0.8,0.1],"text":"neutral sample document number 2 for this supplied dataset."},{"actual":"negative","id":"example-2-0","predicted":"negative","probabilities":[0.1,0.1,0.8],"text":"negative sample document number 0 for this supplied dataset."},{"actual":"negative","id":"example-2-1","predicted":"negative","probabilities":[0.1,0.1,0.8],"text":"negative sample document number 1 for this supplied dataset."},{"actual":"negative","id":"example-2-2","predicted":"negative","probabilities":[0.1,0.1,0.8],"text":"negative sample document number 2 for this supplied dataset."}]}'

# x402 v2 sends the challenge base64-encoded in the PAYMENT-REQUIRED header (v1 sends it in the body).
# To read the header:
curl -sS -g -o /dev/null -D - -X POST 'https://market.datapackvibe.com/x402/assistant-sector-ml-sentiment-log-loss' \
  -H 'content-type: application/json' \
  --data-raw '{"records":[{"actual":"positive","id":"example-0-0","predicted":"positive","probabilities":[0.8,0.1,0.1],"text":"positive sample document number 0 for this supplied dataset."},{"actual":"positive","id":"example-0-1","predicted":"positive","probabilities":[0.8,0.1,0.1],"text":"positive sample document number 1 for this supplied dataset."},{"actual":"positive","id":"example-0-2","predicted":"positive","probabilities":[0.8,0.1,0.1],"text":"positive sample document number 2 for this supplied dataset."},{"actual":"neutral","id":"example-1-0","predicted":"neutral","probabilities":[0.1,0.8,0.1],"text":"neutral sample document number 0 for this supplied dataset."},{"actual":"neutral","id":"example-1-1","predicted":"neutral","probabilities":[0.1,0.8,0.1],"text":"neutral sample document number 1 for this supplied dataset."},{"actual":"neutral","id":"example-1-2","predicted":"neutral","probabilities":[0.1,0.8,0.1],"text":"neutral sample document number 2 for this supplied dataset."},{"actual":"negative","id":"example-2-0","predicted":"negative","probabilities":[0.1,0.1,0.8],"text":"negative sample document number 0 for this supplied dataset."},{"actual":"negative","id":"example-2-1","predicted":"negative","probabilities":[0.1,0.1,0.8],"text":"negative sample document number 1 for this supplied dataset."},{"actual":"negative","id":"example-2-2","predicted":"negative","probabilities":[0.1,0.1,0.8],"text":"negative sample document number 2 for this supplied dataset."}]}' \
  | grep -i '^payment-required:' | cut -d' ' -f2 | tr -d '\r' | base64 --decode

JavaScript: pay and call

// npm i @x402/fetch @x402/evm viem
import { wrapFetchWithPayment, x402Client } from "@x402/fetch";
import { registerExactEvmScheme } from "@x402/evm/exact/client";
import { privateKeyToAccount } from "viem/accounts";

// Pays at most $0.01 per call.
const client = new x402Client().setSpendControls({ maxAmountPerPayment: "$0.01" });
registerExactEvmScheme(client, { signer: privateKeyToAccount(process.env.EVM_PRIVATE_KEY) });
const pay = wrapFetchWithPayment(fetch, client);

// Not sure it's safe to pay? ToolVet checks it first for $0.005. Pay for that from a separate
// client: this one is capped at $0.01 per payment. GET https://toolvet.app/api/v1/check?url=https%3A%2F%2Fmarket.datapackvibe.com%2Fx402%2Fassistant-sector-ml-sentiment-log-loss&method=POST
const res = await pay("https://market.datapackvibe.com/x402/assistant-sector-ml-sentiment-log-loss", {
  method: "POST",
  headers: { "content-type": "application/json" },
  body: JSON.stringify({
    "records": [
      {
        "actual": "positive",
        "id": "example-0-0",
        "predicted": "positive",
        "probabilities": [
          0.8,
          0.1,
          0.1
        ],
        "text": "positive sample document number 0 for this supplied dataset."
      },
      {
        "actual": "positive",
        "id": "example-0-1",
        "predicted": "positive",
        "probabilities": [
          0.8,
          0.1,
          0.1
        ],
        "text": "positive sample document number 1 for this supplied dataset."
      },
      {
        "actual": "positive",
        "id": "example-0-2",
        "predicted": "positive",
        "probabilities": [
          0.8,
          0.1,
          0.1
        ],
        "text": "positive sample document number 2 for this supplied dataset."
      },
      {
        "actual": "neutral",
        "id": "example-1-0",
        "predicted": "neutral",
        "probabilities": [
          0.1,
          0.8,
          0.1
        ],
        "text": "neutral sample document number 0 for this supplied dataset."
      },
      {
        "actual": "neutral",
        "id": "example-1-1",
        "predicted": "neutral",
        "probabilities": [
          0.1,
          0.8,
          0.1
        ],
        "text": "neutral sample document number 1 for this supplied dataset."
      },
      {
        "actual": "neutral",
        "id": "example-1-2",
        "predicted": "neutral",
        "probabilities": [
          0.1,
          0.8,
          0.1
        ],
        "text": "neutral sample document number 2 for this supplied dataset."
      },
      {
        "actual": "negative",
        "id": "example-2-0",
        "predicted": "negative",
        "probabilities": [
          0.1,
          0.1,
          0.8
        ],
        "text": "negative sample document number 0 for this supplied dataset."
      },
      {
        "actual": "negative",
        "id": "example-2-1",
        "predicted": "negative",
        "probabilities": [
          0.1,
          0.1,
          0.8
        ],
        "text": "negative sample document number 1 for this supplied dataset."
      },
      {
        "actual": "negative",
        "id": "example-2-2",
        "predicted": "negative",
        "probabilities": [
          0.1,
          0.1,
          0.8
        ],
        "text": "negative sample document number 2 for this supplied dataset."
      }
    ]
  }),
});
console.log(res.status, await res.text());

Python: pay and call

# pip install "x402[requests,evm]"
import os
from eth_account import Account
from x402 import x402ClientSync
from x402.http.clients import x402_requests
from x402.mechanisms.evm import EthAccountSigner
from x402.mechanisms.evm.exact.register import register_exact_evm_client

# Pays at most $0.01 per call.
client = x402ClientSync().set_spend_controls({"max_amount_per_payment": "$0.01"})
register_exact_evm_client(client, EthAccountSigner(Account.from_key(os.environ["EVM_PRIVATE_KEY"])))

with x402_requests(client) as session:
    res = session.post(
        "https://market.datapackvibe.com/x402/assistant-sector-ml-sentiment-log-loss",
        json={
            "records": [
                {
                    "actual": "positive",
                    "id": "example-0-0",
                    "predicted": "positive",
                    "probabilities": [
                        0.8,
                        0.1,
                        0.1,
                    ],
                    "text": "positive sample document number 0 for this supplied dataset.",
                },
                {
                    "actual": "positive",
                    "id": "example-0-1",
                    "predicted": "positive",
                    "probabilities": [
                        0.8,
                        0.1,
                        0.1,
                    ],
                    "text": "positive sample document number 1 for this supplied dataset.",
                },
                {
                    "actual": "positive",
                    "id": "example-0-2",
                    "predicted": "positive",
                    "probabilities": [
                        0.8,
                        0.1,
                        0.1,
                    ],
                    "text": "positive sample document number 2 for this supplied dataset.",
                },
                {
                    "actual": "neutral",
                    "id": "example-1-0",
                    "predicted": "neutral",
                    "probabilities": [
                        0.1,
                        0.8,
                        0.1,
                    ],
                    "text": "neutral sample document number 0 for this supplied dataset.",
                },
                {
                    "actual": "neutral",
                    "id": "example-1-1",
                    "predicted": "neutral",
                    "probabilities": [
                        0.1,
                        0.8,
                        0.1,
                    ],
                    "text": "neutral sample document number 1 for this supplied dataset.",
                },
                {
                    "actual": "neutral",
                    "id": "example-1-2",
                    "predicted": "neutral",
                    "probabilities": [
                        0.1,
                        0.8,
                        0.1,
                    ],
                    "text": "neutral sample document number 2 for this supplied dataset.",
                },
                {
                    "actual": "negative",
                    "id": "example-2-0",
                    "predicted": "negative",
                    "probabilities": [
                        0.1,
                        0.1,
                        0.8,
                    ],
                    "text": "negative sample document number 0 for this supplied dataset.",
                },
                {
                    "actual": "negative",
                    "id": "example-2-1",
                    "predicted": "negative",
                    "probabilities": [
                        0.1,
                        0.1,
                        0.8,
                    ],
                    "text": "negative sample document number 1 for this supplied dataset.",
                },
                {
                    "actual": "negative",
                    "id": "example-2-2",
                    "predicted": "negative",
                    "probabilities": [
                        0.1,
                        0.1,
                        0.8,
                    ],
                    "text": "negative sample document number 2 for this supplied dataset.",
                },
            ],
        },
    )
    print(res.status_code, res.text)

Security scan

No findings

We scan names, descriptions and tool definitions for hidden instructions and other prompt-injection patterns.

Recent checks

When	Result	HTTP	Time	Price
3 h ago	Passed	402	90 ms	$0.01
7 h ago	Passed	402	97 ms	$0.01
12 h ago	Passed	402	76 ms	$0.01
16 h ago	Passed	402	2.9 s	$0.01
21 h ago	Passed	402	119 ms	$0.01

Show 10 older checks

25 h ago	Passed	402	619 ms	$0.01
29 h ago	Passed	402	97 ms	$0.01
33 h ago	Passed	402	108 ms	$0.01
38 h ago	Passed	402	69 ms	$0.01
42 h ago	Passed	402	56 ms	$0.01
46 h ago	Passed	402	66 ms	$0.01
2 days ago	Passed	402	67 ms	$0.01
2 days ago	Passed	402	1.5 s	$0.01
2 days ago	Expected HTTP 402, got 530	530	40 ms	-
3 days ago	Expected HTTP 402, got 530	530	193 ms	-

Trust score 71/ 100

Availability (30-day uptime)		23 / 30
Valid x402 challenge		15 / 15
Response time		10 / 10
Real paying agents (30 days)		3 / 15
Charges what it lists		10 / 10
Security scan		10 / 10
Verified owner		0 / 10

How the score works

Payment terms

Price: $0.01 per call
Listed at: $0.01
Token: USDC
Networks: Base
Pays to: 0x8bc2…3C8C

Real usage

Paying agents: 1 in 30 days
Paid calls: 1 in 30 days
Last paid call: 13 days ago

Source: x402 Bazaar settlement data.

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Trust score

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