distilbert-sst-2-int8

Text Classification • HuggingFace • Hosted

Distilled BERT model that was finetuned on SST-2 for sentiment classification

Model Info
More information	link ↗
Unit Pricing	$0.026 per M input tokens

Usage

export interface Env {
  AI: Ai;
}

export default {
  async fetch(request, env): Promise<Response> {

    const response = await env.AI.run(
      "@cf/huggingface/distilbert-sst-2-int8",
      {
        text: "This pizza is great!",
      }
    );

    return Response.json(response);
  },
} satisfies ExportedHandler<Env>;

API_BASE_URL = "https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai/run/"
headers = {"Authorization": "Bearer {API_KEY}"}

def run(model, input):
    response = requests.post(f"{API_BASE_URL}{model}", headers=headers, json=input)
    return response.json()

output = run("@cf/huggingface/distilbert-sst-2-int8", { "text": "This pizza is great!" })
print(output)

curl https://api.cloudflare.com/client/v4/accounts/$CLOUDFLARE_ACCOUNT_ID/ai/run/@cf/huggingface/distilbert-sst-2-int8  \
  -X POST  \
  -H "Authorization: Bearer $CLOUDFLARE_API_TOKEN"  \
  -d '{ "text": "This pizza is great!" }'

Parameters

Input
Output

text

stringrequiredminLength: 1The text that you want to classify

type

array

contentType

application/json

description

An array of classification results for the input text

items

[object Object]

{
  "type": "object",
  "properties": {
    "text": {
      "type": "string",
      "minLength": 1,
      "description": "The text that you want to classify"
    }
  },
  "required": [
    "text"
  ]
}

{
  "type": "array",
  "contentType": "application/json",
  "description": "An array of classification results for the input text",
  "items": {
    "type": "object",
    "properties": {
      "score": {
        "type": "number",
        "description": "Confidence score indicating the likelihood that the text belongs to the specified label"
      },
      "label": {
        "type": "string",
        "description": "The classification label assigned to the text (e.g., 'POSITIVE' or 'NEGATIVE')"
      }
    }
  }
}