Add model card for TIC-FM

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+ ---
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+ pipeline_tag: other
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+ ---
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+
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+ # TIC-FM
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+ This repository contains the pre-trained weights and configuration for **TIC-FM** (Time Series In-Context Foundation Model), presented in the paper [Rethinking Zero-Shot Time Series Classification: From Task-specific Classifiers to In-Context Inference](https://huggingface.co/papers/2602.00620).
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+ - **Repository:** [GitHub - fangjuntao/TIC-FM](https://github.com/fangjuntao/TIC-FM)
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+ - **Paper:** [Hugging Face Papers](https://huggingface.co/papers/2602.00620)
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+
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+ ## Model Description
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+ TIC-FM is an in-context learning framework that treats the labeled training set as context and predicts labels for test instances in a single forward pass without requiring parameter updates or fine-tuning. It pairs a time series encoder and a lightweight projection adapter with a split-masked latent memory Transformer.
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+
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+ ## Quick Start & Evaluation
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+ To set up the environment and run evaluations with this checkpoint, clone the official repository and set up the conda environment:
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+ ```bash
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+ git clone https://github.com/fangjuntao/TIC-FM.git
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+ cd TIC-FM
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+ conda create -n TICFS python=3.9 -y
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+ conda activate TICFS
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+ pip install torch numpy
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+ ```
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+
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+ To run the evaluation script on the UCR datasets using the downloaded checkpoint:
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+ ```bash
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+ python scripts/eval_TSEncoder_orion_icl_classifier_ucr_full.py \
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+ --suite ucr \
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+ --mode classifier_v2 \
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+ --ucr_path /path/to/UCRdata/ \
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+ --full_ckpt checkpoints/TSEncoder_orion_icl_full.pt \
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+ --model_hparams_json checkpoints/TSEncoder_orion_icl_model_hparams.json
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+ ```
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+
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+ To evaluate a single dataset:
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+
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+ ```bash
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+ python scripts/eval_TSEncoder_orion_icl_classifier_ucr_full.py \
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+ --suite ucr \
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+ --dataset ECG200 \
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+ --mode direct
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+ ```
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+
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+ ## Citation
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+
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+ ```bibtex
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+ @article{fang2026rethinking,
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+ title={Rethinking Zero-Shot Time Series Classification: From Task-specific Classifiers to In-Context Inference},
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+ author={Fang, Juntao and others},
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+ journal={arXiv preprint arXiv:2602.00620},
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+ year={2026}
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+ }
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+ ```