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Nexdata | Multilingual Fine-Tuning Text Data | 2 Millions | Foundation Model | SFT Data | Large Language Model(LLM) Data
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Description
Country Coverage
History
Volume
2 million | pairs |
Pricing
License | Starts at |
---|---|
One-off purchase |
$20,000 / purchase |
Monthly License | Not available |
Yearly License | Not available |
Usage-based | Not available |
Suitable Company Sizes
Quality
Delivery
Use Cases
Categories
Related Searches
Related Products
Frequently asked questions
What is Nexdata Multilingual Fine-Tuning Text Data 2 Millions Foundation Model SFT Data Large Language Model(LLM) Data?
Off-the-shelf 2 millions pairs SFT text data. Contains 12 types of SFT QA, and the accuracy is not less than 95%. All prompts are manually written to meet diversity coverage.
What is Nexdata Multilingual Fine-Tuning Text Data 2 Millions Foundation Model SFT Data Large Language Model(LLM) Data used for?
This product has 5 key use cases. Nexdata recommends using the data for Artificial Intelligence (AI), Machine Learning (ML), Deep Learning, Generative AI, and LLM Training. Global businesses and organizations buy Natural Language Processing (NLP) Data from Nexdata to fuel their analytics and enrichment.
Who can use Nexdata Multilingual Fine-Tuning Text Data 2 Millions Foundation Model SFT Data Large Language Model(LLM) Data?
This product is best suited if you’re a Medium-sized Business or Enterprise looking for Natural Language Processing (NLP) Data. Get in touch with Nexdata to see what their data can do for your business and find out which integrations they provide.
How far back does the data in Nexdata Multilingual Fine-Tuning Text Data 2 Millions Foundation Model SFT Data Large Language Model(LLM) Data go?
This product has 5 years of historical coverage. It can be delivered on a secondly, minutely, hourly, daily, weekly, monthly, quarterly, yearly, real-time, and on-demand basis.
Which countries does Nexdata Multilingual Fine-Tuning Text Data 2 Millions Foundation Model SFT Data Large Language Model(LLM) Data cover?
This product includes data covering 50 countries like USA, Japan, Germany, United Kingdom, and France. Nexdata is headquartered in United States of America.
How much does Nexdata Multilingual Fine-Tuning Text Data 2 Millions Foundation Model SFT Data Large Language Model(LLM) Data cost?
Pricing for Nexdata Multilingual Fine-Tuning Text Data 2 Millions Foundation Model SFT Data Large Language Model(LLM) Data starts at USD20,000 per purchase. Connect with Nexdata to get a quote and arrange custom pricing models based on your data requirements.
How can I get Nexdata Multilingual Fine-Tuning Text Data 2 Millions Foundation Model SFT Data Large Language Model(LLM) Data?
Businesses can buy Natural Language Processing (NLP) Data from Nexdata and get the data via S3 Bucket, SFTP, Email, UI Export, REST API, SOAP API, Streaming API, and Feed API. Depending on your data requirements and subscription budget, Nexdata can deliver this product in .bin, .json, .xml, .csv, .xls, .sql, and .txt format.
What is the data quality of Nexdata Multilingual Fine-Tuning Text Data 2 Millions Foundation Model SFT Data Large Language Model(LLM) Data?
Nexdata has reported that this product has the following quality and accuracy assurances: 95% Accuracy. You can compare and assess the data quality of Nexdata using Datarade’s data marketplace.
What are similar products to Nexdata Multilingual Fine-Tuning Text Data 2 Millions Foundation Model SFT Data Large Language Model(LLM) Data?
This product has 3 related products. These alternatives include Nexdata Multilingual Text Questions Data 50 Millions Foundation Model Unsupervised Text Data Large Language Model(LLM) Data, Large Language Model (LLM) Data Machine Learning (ML) Data AI Training Data (RAG) for 1M+ Global Grocery, Restaurant, and Retail Stores, and AI & ML Training Data 800M Profiles for LLMs, Generative AI, NLP & Predictive Models. You can compare the best Natural Language Processing (NLP) Data providers and products via Datarade’s data marketplace and get the right data for your use case.