Model Card
Lelapa-X-Search
Model Details
Basic information about the model: Review section 4.1 of the model cards paper.
| Organization | Lelapa AI |
|---|---|
| Product | Vulavula |
| Model date | 1 November 2024 |
| Feature | Search |
| Lang | N/A |
| Domain | General |
| Model Name | Lelapa-X-Search |
| Model version | 1.0.0 |
| Model Type | Proprietary Model |
Information about training algorithms, parameters, fairness constraints or other applied approaches, and features: Proprietary
License: Proprietary
Contact: info@lelapa.ai
Intended use
Use cases that were envisioned during development: Review section 4.2 of the model cards paper.
Primary intended uses
Information retrieval from heterogeneous knowledge bases to enable search capabilities.
Primary intended users
Intended cases include catalog or document search, data exploration, and retrieval-augmented generation (RAG) for conversational search.
Out-of-scope use cases
All tasks outside of intelligent search.
Factors
Factors could include demographic or phenotypic groups, environmental conditions, technical attributes, or others listed in Section 4.3: Review section 4.3 of the model cards paper.
Relevant factors
Given a knowledge base there are two primary workloads which include indexing and querying. Indexing loads content and makes it searchable, while querying is done on the searchable content.
Evaluation factors
To be defined.
Metrics
The appropriate metrics to feature in a model card depend on the model being tested. For example, classification systems in which the primary output is a class label differ significantly from systems whose primary output is a score. In all cases, the reported metrics should be determined based on the model’s structure and intended use: Review section 4.4 of the model cards paper.
Model performance measures
Not specified.
Decision thresholds
N/A
Evaluation data
All referenced datasets would ideally point to any set of documents that provide visibility into the source and composition of the dataset. Evaluation datasets should include datasets that are publicly available for third-party use. These could be existing datasets or new ones provided alongside the model card analyses to enable further benchmarking.
Review section 4.5 of the model cards paper.
In Progress.
Training data
Review section 4.6 of the model cards paper.
Proprietary.
Quantitative analyses
Quantitative analyses should be disaggregated, that is, broken down by the chosen factors. Quantitative analyses should provide the results of evaluating the model according to the chosen metrics, providing confidence interval values when possible.
Review section 4.7 of the model cards paper.
Unitary results
In progress.
Intersectional result
In progress.
Ethical considerations
This section is intended to demonstrate the ethical considerations that went into model development, surfacing ethical challenges and solutions to stakeholders. The ethical analysis does not always lead to precise solutions, but the process of ethical contemplation is worthwhile to inform on responsible practices and next steps in future work: Review section 4.8 of the model cards paper.
N/A.
Caveats and recommendations
This section should list additional concerns that were not covered in the previous sections.
Review section 4.9 of the model cards paper.
Additional caveats are outlined extensively in our Terms and Conditions.