--- license: apache-2.0 language: en tags: - temporal - time-module - pieces - intent-classification - temporal-intent datasets: - Pieces/temporal-intent-classification-dataset-split base_model: google/flan-t5-small metrics: - accuracy - f1 pipeline_tag: text-classification --- # TIME-Module: Classification — flan-t5-small ## Model Description Temporal intent classification model trained on the split dataset. Classifies user queries into 6 temporal intent categories: Action Scheduling, Content Retrieval, Current Status, Future Information/Planning, Non-Temporal, and Temporal - General. ## Training Details - **Base Model:** [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) - **Architecture:** T5ForSequenceClassification - **Dataset:** [Pieces/temporal-intent-classification-dataset-split](https://huggingface.co/datasets/Pieces/temporal-intent-classification-dataset-split) (15,488 train / 1,721 val / 4,303 test) - **Training Steps:** 17,500 - **Learning Rate:** 3e-4 - **Batch Size:** 64 × 2 - **Mixed Precision:** bf16 - **Hardware:** NVIDIA RTX 4090 (24 GB) ## Results | Metric | Value | |--------|-------| | accuracy | 95.91% | | f1_weighted | ~95.9% | ## Usage ```python from transformers import pipeline pipe = pipeline('text-classification', model='Pieces/time-classification-flan-t5-small-split-best') result = pipe('What meetings do I have tomorrow?') ``` ## Part of the TIME-Module Project This model is part of the TIME (Temporal Intent, Mapping, and Extraction) module, a suite of models for understanding and processing temporal information in natural language. **Related models:** - [Pieces/time-classification-flan-t5-small-split-best](https://huggingface.co/Pieces/time-classification-flan-t5-small-split-best) — Intent classification - [Pieces/time-mapping-flan-t5-small-quality-best](https://huggingface.co/Pieces/time-mapping-flan-t5-small-quality-best) — Span prediction (best) - [Pieces/time-mapping-t5gemma-270m-best](https://huggingface.co/Pieces/time-mapping-t5gemma-270m-best) — Span prediction (T5Gemma) ## Citation ```bibtex @software{time_module, title={TIME-Module: Temporal Intent, Mapping, and Extraction}, author={Pieces}, year={2026}, url={https://huggingface.co/Pieces} } ```