distilbert-sst-2-int8
Text Classification • HuggingFaceDistilled BERT model that was finetuned on SST-2 for sentiment classification
Usage
Workers - TypeScript
  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>;Python
  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
  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
-  textstring min 1The text that you want to classify 
Output
-  itemsobject-  scorenumberConfidence score indicating the likelihood that the text belongs to the specified label 
-  labelstringThe classification label assigned to the text (e.g., 'POSITIVE' or 'NEGATIVE') 
 
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API Schemas
The following schemas are based on JSON Schema
{    "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')"            }        }    }}