# Teradata Word Embeddings

> Word and document embeddings for text analytics

- Skill: `teradata-labs/teradata-word-embeddings` (Agent Skill, multi-file: 11 files)
- Install (CLI): `npx skillmds@latest add teradata-labs/teradata-word-embeddings`
- Raw SKILL.md: https://api.skillmd.com/api/skills/teradata-labs/teradata-word-embeddings/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Data & Analytics
- Author: teradata-labs (https://skillmd.com/u/teradata-labs)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/teradata-labs/teradata-word-embeddings

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# Teradata Word Embeddings

| Property | Value |
|----------|-------|
| **Skill Name** | Teradata Word Embeddings |
| **Description** | Word and document embeddings for text analytics |
| **Category** | Text Analytics |
| **Primary Function** | TD_WordEmbeddings |
| **Framework** | SQLE |

## Core Capabilities

- Automated table structure analysis via DBC.ColumnsV
- Dynamic SQL generation for TD_WordEmbeddings
- Complete workflow from data preparation to results interpretation
- Data quality validation and preprocessing guidance
- Parameter optimization and tuning

## Key Parameters

- **TextColumn**: Column containing text data
- **Operation**: 'TOKEN-EMBEDDING','DOC-EMBEDDING','TOKEN2TOKEN-SIMILARITY','DOC2DOC-SIMILARITY'
- **EmbeddingModelTable**: Pre-trained embedding model (e.g., GloVe) stored as a table
- **Accumulate**: Columns to pass through

## Use Cases

1. Document similarity analysis
2. Text classification feature extraction
3. Semantic search
4. Content recommendation
5. Sentiment analysis preprocessing

## Example Usage

```sql
-- TD_WordEmbeddings execution
SELECT * FROM TD_WordEmbeddings (
    ON {USER_DATABASE}.{TEXT_TABLE} AS InputTable
    ON {USER_DATABASE}.{EMBEDDING_MODEL_TABLE} AS EmbeddingModelTable DIMENSION
    USING
    TextColumn ('{TEXT_COLUMN}')
    Operation ('DOC-EMBEDDING')        -- 'TOKEN-EMBEDDING','DOC-EMBEDDING',
                                       -- 'TOKEN2TOKEN-SIMILARITY','DOC2DOC-SIMILARITY'
    Accumulate ('{DOC_ID}')
) AS dt;
```

## Scripts Included

### Core Analytics Scripts
- **`table_analysis.sql`**: Automatic table structure discovery
- **`preprocessing.sql`**: Data preparation and feature engineering
- **`model_training.sql`**: TD_WordEmbeddings execution
- **`evaluation.sql`**: Results analysis and metrics
- **`complete_workflow_template.sql`**: End-to-end workflow

### Utility Scripts
- **`data_quality_checks.sql`**: Comprehensive data validation
- **`parameter_tuning.sql`**: Parameter optimization
- **`diagnostic_queries.sql`**: Results diagnostics and interpretation

## Best Practices

- Always run table_analysis.sql first to understand your data structure
- Validate data quality before executing the analytical function
- Use parameter_tuning.sql to find optimal configuration
- Review diagnostic_queries.sql output for model/results validation

## Limitations

- Requires Teradata Vantage 17.20+ with ClearScape Analytics
- Input data must meet function-specific requirements
- Results depend on data quality and parameter configuration

*Teradata Word Embeddings - ClearScape Analytics skill for Teradata Vantage*