TSDAE fine-tuned model on civic/government meeting transcripts. The model was trained on 10,000 government meeting transcripts to learn domain-specific representations while remaining general-purpose for downstream tasks.

Key Features

✅ Learned domain-specific abbreviations (BOS = Board of Supervisors, etc.)
✅ Enhanced understanding of government/civic terminology
✅ General-purpose embeddings suitable for various downstream tasks

Usage

python

from sentence_transformers import SentenceTransformer

model = SentenceTransformer('TSG/all-minilm-l12-v2-civic')
embeddings = model.encode(['Your text here'])

Training Data

10,000 government/civic meeting transcripts
Various meeting types: City Council, Planning Commission, Board of Education, etc.
Text cleaned and chunked for training

Evaluation

The model shows significant improvement in:

Domain-specific semantic similarity
Abbreviation understanding (BOS, CC, BOE, etc.)
Clustering quality for civic domain

Intended Use

This model is designed for general-purpose semantic similarity and embedding tasks, with enhanced understanding of government/civic domain language.

Original Model

This model was originally trained and hosted at: https://huggingface.co/ar9av/all-minilm-l12-v2-civic