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- The TechQA Dataset for ACL 2020 - IBM Research
We introduce TECHQA, a domain-adaptation question answering dataset for the technical support domain The TECHQA corpus highlights two real-world issues from the automated customer support domain
- How technical questions are handled by IBM Support
Technical question support allows you to obtain assistance from IBM for product-specific, task-oriented questions regarding the installation and operation of currently supported IBM software and hardware
- GitHub - IBM techqa: The TechQA dataset -- http: ibm. biz Tech_QA
In order to train a model on TechQA, use the script below Note: Since TechQA is smaller dataset, it is better to start with a model that is already trained on a bigger QA dataset
- The TechQA Dataset - ACL Anthology
We introduce TECHQA, a domain-adaptation question answering dataset for the technical support domain The TECHQA corpus highlights two real-world issues from the automated customer support domain
- The TechQA Dataset - arXiv. org
We introduce TECHQA, a domain-adaptation question answering dataset for the technical support domain The TECHQA corpus high-lights two real-world issues from the auto-mated customer support domain
- The TechQA Dataset | DeepAI
The dataset was obtained by crawling the IBM Developer and IBM DeveloperWorks forums for questions with accepted answers that appear in a published IBM Technote—a technical document that addresses a specific technical issue
- Adding vectorized documents for grounding foundation model prompts
When you add grounding documents to create a new vector index, you can upload files or connect to a data asset that contains files The following table lists the supported file types and maximum file sizes that you can add when you create a new vector index
- Question answering for enterprise use cases - IBM Research
To address this, IBM Research AI is introducing a new leaderboard called TechQA which uses real world questions from users posted on IBM DeveloperWorks The goal of TechQA is to foster research on enterprise QA, where learning from a relatively small set of QA pairs is the more realistic condition
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