OCTOBER 19TH - 21ST: TransformX 2022 - Operationalizing AI and Machine Learning by Scale

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Text

The sophisticated platform for text annotation.

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Use Cases

Natural Language Processing

Extract information and gain valuable insights from a vast corpus of text-based data. Understand and interact with text to improve search relevance, automate customer support and more.

Information Extraction
Extract structured text information from unstructured or semi-structured content.
- • Document Processing
- • Topic Modeling
- • Product Cataloguing
- • Policy Enforcement
Natural Language Generation
Understand text inputs, then generate appropriate responses.
- • Search and Ad Relevance
- • Question Answering (Virtual Assistants)
- • Sentiment & Intent Analysis
- • Content Summarization & Moderation
Machine Translation
Automatically translate text inputs into different languages.
- • Automated Translation
- • Automated Transcription
- • Dialogue Generation
- • Speech to Text, Text to Speech

How it works

Easy to Start, Optimize and Scale

Build models you can trust while maximizing operational efficiency and reducing the cost of ML projects.

Named Entity Recognition
Classification
Multi-lingual Transcription
Multi-lingual Data Collection

Please label any people, places, or organizations in the following text.

1client.*createNamedentityrecognitionTask*({
2  callback_url: 'http://www.example.com/callback',
3  instruction: 'Please label any people, places, or organizations in the following text.',
4  params: {
5    text: "They were delighted by the Hilton's location: surrounded by vibrant nightlife, and in close proximity to major landmarks in Paris. And as luck would have it, Paris Hilton happened to be staying in the room next door!",
6    labels: [
7      {
8        name: 'T_ORG',
9        display_name: 'Organization',
10      },
11      {
12        name: 'T_LOC',
13        display_name: 'Location',
14      },
15      {
16        name: 'T_PERS',
17        display_name: 'Person',
18      },
19    ]
20  },
21}, (err, task) => {
22    // do something with task
23});

Run Extraction

Automated Quality Pipeline
Tech-enabled quality assurance systems built into the product to reduce costly human review without sacrificing quality. Customers are also provided transparency in regards to quality with a QA tool and dashboard metrics.
Confidence-Based Consensus
Scale Text combines confidence-based consensus and balanced dataset curation to reduce bias and to surface ambiguities early. For more on how we mitigate bias, take a look at <a href="https://scale.com/blog/if-youre-de-biasing-the-model-its-too-late"> our blog post</a>.
Multi-Lingual Support
A large, globally distributed workforce of highly trained labelers gives Scale Text the flexibility to support multiple languages, regional context and unique expertise.

“OpenAI threw a bunch of tasks at Scale AI with difficult characteristics, including tight latency requirements and significant ambiguity in correct answers. In response, Scale worked closely with us to adjust their QA systems to our needs.”

Geoffrey Irving

Member of Technical Staff, OpenAI

Enterprise Ready

Custom Annual Plans and SLAs

Get started today with on-demand, or chat with us about an enterprise plan.

Guaranteed Task Completion Time
Enterprise-grade SLAs include task completion times and tasks can be rapidly scaled up and down to meet your requirements.
24/7 Development Support
Each enterprise customer is paired with a dedicated engagement manager who will ensure smooth on-boarding and continued data delivery.
Cost Effective
Enterprise engagements provide upfront and volume-based discounts, and is the most cost-effective solution for high-quality labels. Plus with Scale AI, there are no platform fees.

Quality Assurance

Best-In-Class Quality

ML-accelerated, human-in-the-loop data annotation for industry-leading quality.

Super Human Quality

Text tasks submitted to the platform combines confidence-based consensus and balanced dataset curation to reduce bias and surface ambiguities early. All tasks receive additional layers of both ML-based checks and human review based on the quality confidence scores.

The resulting accuracy is consistently higher than what a human or synthetic labeling approach can achieve independently.