# Language Detector (`zsoftware/language-detector`) Actor Detect the language of each line of text using machine learning. Paste multiple lines of text into the input, and this actor will identify the language of each one, returning results with confidence scores and alternative guesses based on a trained statistical model. - **URL**: https://apify.com/zsoftware/language-detector.md - **Developed by:** [Karim](https://apify.com/zsoftware) (community) - **Categories:** Developer tools, Automation - **Stats:** 16 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Language Detector Actor This actor detects the language of each line of text using machine learning techniques. It allows users to input multiple lines of text, with each line representing a separate text sample. The actor will return the detected language for each line, along with confidence scores and alternative language guesses. ### Features - **Language Detection**: Automatically detects the language of each line of text. - **Confidence Scores**: Provides the likelihood of the language detection for each guess. - **Alternative Guesses**: In case the first guess isn't 100% accurate, it returns additional language options with probabilities. - **Multi-line Support**: Accepts multiple text lines as input (one per line), with each line being processed individually. ### Input - **Text**: Paste or enter multiple lines of text (one text sample per line). Each line will be processed separately to detect the language. #### Example Input ```` Hello, how are you? Bonjour, comment ça va? これは日本語です。 ```` ### Output The actor will return the detected language for each line of text, along with alternative language guesses and their confidence scores. #### Example Output ```json [ { "text": "Hello, how are you?", "language": "en", "confidence": 0.999995 }, { "text": "Bonjour, comment ça va?", "language": "fr", "confidence": 0.999991 }, { "text": "Hola, ¿cómo estás?", "language": "es", "confidence": 0.999995 }, { "text": "これは日本語です。", "language": "ja", "confidence": 1.0 } ] ```` ### How It Works 1. **Input Processing**: Users input multiple lines of text (one line per text sample). Each line is parsed and passed to the language detection model. 2. **Language Detection**: The actor uses a **machine learning model** (Naive Bayes classifier) to detect the language based on n-gram patterns found in the input. 3. **Output**: For each line of text, the actor returns the detected language, along with the confidence score and alternative guesses. ### Deployment This actor is deployed on Apify and can be used via the **Apify Console**. Once deployed, users can provide text as input and retrieve language detection results through the UI. ### Usage 1. Go to the Apify Console. 2. Run the actor with your text input. 3. Download the results or view them in the Apify UI. ### Limitations - The actor is best suited for shorter texts, such as sentences or short paragraphs. - Accuracy can vary with very short or ambiguous text inputs. # Actor input Schema ## `text` (type: `string`): Enter one text sample per line. Each line will be analyzed as a separate input. ## Actor input object example ```json { "text": "Hello, how are you?\nBonjour, comment ça va?\nHola, ¿cómo estás?\nこれは日本語です。" } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "text": `Hello, how are you? Bonjour, comment ça va? Hola, ¿cómo estás? これは日本語です。` }; // Run the Actor and wait for it to finish const run = await client.actor("zsoftware/language-detector").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "text": """Hello, how are you? Bonjour, comment ça va? Hola, ¿cómo estás? これは日本語です。""" } # Run the Actor and wait for it to finish run = client.actor("zsoftware/language-detector").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "text": "Hello, how are you?\\nBonjour, comment ça va?\\nHola, ¿cómo estás?\\nこれは日本語です。" }' | apify call zsoftware/language-detector --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=zsoftware/language-detector", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Language Detector", "description": "Detect the language of each line of text using machine learning.\nPaste multiple lines of text into the input, and this actor will identify the language of each one, returning results with confidence scores and alternative guesses based on a trained statistical model.", "version": "1.0", "x-build-id": "yZK0TvFy9C8AZGoil" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/zsoftware~language-detector/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-zsoftware-language-detector", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/zsoftware~language-detector/runs": { "post": { "operationId": "runs-sync-zsoftware-language-detector", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/zsoftware~language-detector/run-sync": { "post": { "operationId": "run-sync-zsoftware-language-detector", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "text" ], "properties": { "text": { "title": "Text Input", "type": "string", "description": "Enter one text sample per line. Each line will be analyzed as a separate input.", "default": "Hello, how are you?\nBonjour, comment ça va?\nHola, ¿cómo estás?\nこれは日本語です。" } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```