How It Works
DataForce’s generative AI model training services help businesses of all sizes and industries solve complex problems, create novel designs, and produce unique content through generative AI models. Our solutions enable high-quality AI outputs through customized data services in multiple modalities, including speech, audio, text, image, and video. Whether you are developing new foundational models, such as large language models (LLMs), or customizing an existing model for a new use case, DataForce tailors solutions that address the unique data challenges your organization faces.
• • • • • •Modalities and Services• • • • • •
|Text + Audio
|Text + Audio
|Text + Image
|Text + Video
|TTS, Virtual Agents
|Services for Developing Generative AI Models
|Large-scale text data collection & cleaning
|Speech & text data collection & annotation
|Large-scale music data collection, classification, and transcription
|Large-scale image data collection & classification
|Large-scale video data collection & classification
Holistic AI/ML Data Services.
- Reinforcement Learning from Human Feedback (RLHF) for LLMs
- Data Relevance
- Data Rating
- Data Moderation
- Audio Data
- Image & Video Data
Reinforcement Learning from Human Feedback (RLHF) for LLMs
Incorporate human feedback into large language models to fine-tune the model's output. This process includes training the model to optimize for specific metrics, such as fluency, relevance, coherence, and appropriateness while also incorporating feedback from human evaluators to improve the quality of the generated text. This feedback is then used to adjust a model’s internal parameters, improving its ability to generate more relevant and coherent responses over time.
Train your model to understand the intent behind a user's search or an advertisement to generate relevant content accordingly. DataForce can analyze user behavior and historical data to identify patterns and preferences, allowing the model to optimize for relevance and improve user experience.
Improve the quality of your AI model’s generated outputs using human review, including ratings and explicit, qualitative feedback. By incorporating this feedback into its training, the AI model can learn to produce outputs that better meet the desired quality standards.
Ensure your model generates outputs that are ethical, unbiased, and do not perpetuate harmful stereotypes or misinformation. This involves carefully curating and filtering the training data to remove any offensive or sensitive content and incorporating diverse perspectives to ensure a more comprehensive and representative output.
Fine-tune your model to generate an output that is tailored to specific linguistic and cultural contexts for chatbots and other large language models. This process includes training the model on local idioms, colloquialisms, and expressions to produce more authentic and relevant output for a particular region or language. With recording studios, offices, and secure facilities in 140+ cities and 46 countries, DataForce can scale globally with accessibility to 250+ languages.
Train your model utilizing techniques such as automatic speech recognition (ASR) and text-to-speech (TTS) to provide the model with spoken language patterns and improve its ability to generate more natural-sounding speech.
Image & Video Data
Utilize techniques such as computer vision and deep learning to train your model on image and video features, patterns, and aesthetics, and improve its ability to generate realistic and high-quality content.
DataForce Services for Creating Foundational Generative AI Models.
We've partnered with thousands of companies around the world. View a selection of our customer stories below to learn more about how DataForce has helped clients excel in the global marketplace.
Onboard 0-1000 annotators within weeks of project kickoff, leveraging a global community of over 1.3 million contributors
Your data is secure with SSAE 16 SOC 2, ISO 27001, HIPAA & GDPR
Integrate directly with the DataForce annotation platform, yours or a third party of your preference
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