整合
將 Potato 與 AI 模型、眾包平臺連線，並匯出到您常用的機器學習框架。
AI 與大語言模型整合
用 AI 輔助增強標註效率
Local LLMs (Ollama)
Run AI-assisted annotation with local LLMs using Ollama. Keep your data completely private.
檢視文件 →HuggingFace
Access open-source models via HuggingFace Inference API for flexible AI assistance.
檢視文件 →LangChain
Automatic trace ingestion from LangChain agents via callback handler. Capture full agent runs as annotation-ready traces.
檢視文件 →OpenAI Vision
OpenAI vision models for multimodal annotation assistance on images and screenshots.
檢視文件 →AI 驅動功能
- 智慧標籤建議
- 自動關鍵詞高亮
- 品質檢查輔助
- 預標註稽核
- 解釋生成
- 一致性檢查
標註團隊方案
使用您自己的團隊或通過眾包擴充套件規模
您自己的團隊
推薦用於敏感資料在本地或您自己的伺服器上執行 Potato，使用內部標註員。非常適合不能外洩的敏感資料、通過 IRB 審批的研究，或已有訓練有素的標註團隊的情況。
優勢
或通過眾包平臺擴充套件規模
Prolific
Academic-friendly crowdsourcing with quality participants. Full integration with completion URLs and participant tracking.
功能特性
SONA Systems
University subject pools. Participants arrive with a SONA survey code, and Potato grants their credit from the server when they finish.
功能特性
Connect and other panels
Providers for CloudResearch Connect, Clickworker and Microworkers, and a generic provider for any panel that passes a participant ID.
功能特性
支援的資料格式
以任何常見格式匯入資料
Text
.txt, .json, .jsonl
Images
.jpg, .png, .gif, .webp
Audio
.mp3, .wav, .ogg, .m4a
Video
.mp4, .webm, .mov
Documents
.pdf, .html
匯出格式
將標註匯出為主流機器學習格式
General
- JSONL
One JSON object per annotator per item
- CSV
Tabular export for spreadsheet analysis
- Parquet
Columnar format for pandas, DuckDB or Spark
NLP
- CoNLL
Standard format for NER and sequence labeling
- Hugging Face
Push annotations to the Hub as a dataset
- spaCy (via CoNLL)
Export CoNLL, then run spacy convert
Computer Vision
- COCO
MS COCO format for object detection
- YOLO
YOLO format for real-time detection
- Pascal VOC
XML bounding boxes, one file per image
Agent Trace Formats
Import agent traces from 15 frameworks for annotation. Convert via CLI or ingest in real time via webhook.
Agent Frameworks
- LangChain / LangSmith
Hierarchical runs, tool calls
- Langfuse
Observation spans, scores
- OpenAI
Function calling, assistants
- Anthropic Claude
Tool use, thinking blocks
- MCP
Model Context Protocol sessions
- OpenTelemetry
Distributed span hierarchy
- ATIF
Standard interchange format
Web Agents
- WebArena
Screenshots, element targeting
- Web Agent
Mind2Web, Computer Use, raw recordings
Coding Agents
- Claude Code
Anthropic Messages API with tool_use
- Aider
Markdown chat with edit blocks
- SWE-Agent
Thought/action/observation trajectories
- SWE-bench
Model and gold patches, test lists
General
- ReAct
Generic thought/action/observation
- Multi-Agent
CrewAI, AutoGen, LangGraph
Agent Training Exports
Export agent annotations directly to training pipeline formats
命令列與 Python
用於自動化的程式設計介面
命令列
# Check a config for errors
potato validate config.yaml --strict
# Start the annotation server
potato start config.yaml
# Export annotations to COCO
python -m potato.export -c config.yaml \
-f coco -o exports/在 Python 中載入匯出檔案
# Export to CSV first:
# python -m potato.export -c config.yaml -f csv -o exports/
import pandas as pd
# One row per annotator per item
df = pd.read_csv("exports/annotations.csv")
print(df.groupby("user_id").size())