SLIP: Sensor Language-Informed Pretraining

Learning Transferable Sensor Models via Language-Informed Pretraining

Yuliang Chen, Arvind Pillai, Yu Yvonne Wu, Tess Z. Griffin, Lisa Marsch, Michael V. Heinz, Nicholas C. Jacobson, Andrew Campbell

Dartmouth College

[Paper] [Code] [Dataset] [SFT Dataset]

---

Overview

SLIP is a multimodal pretraining framework that learns language-aligned sensor representations transferable across diverse sensor setups. It integrates CLIP-style contrastive alignment with sensor-conditioned captioning, enabling both discriminative understanding and generative reasoning over multivariate time series from heterogeneous sensors.

Key features:

• FlexMLP: A weight-sharing patch embedding that dynamically adapts to different temporal resolutions and variable-length inputs without retraining
• Repurposed decoder-only LLM: Splits a pretrained Gemma-3-270M into a unimodal text encoder (first 12 layers) and a multimodal decoder (last 6 layers with cross-attention), enabling efficient sensor-conditioned text generation
• Contrastive + Captioning pretraining: Joint CLIP-style contrastive loss and autoregressive captioning loss for both discriminative and generative capabilities
• Cross-domain transfer: Pretrained on 600K+ sensor-caption pairs (~1B time points) spanning health, environment, IoT, energy, and transportation

Architecture

SLIP comprises four components:

1. Sensor Encoder (120M params): Transformer with FlexMLP patch embedding and 2D RoPE for cross-sensor and long-range temporal interactions
2. Sensor Pooler: Attention pooling with 65 learnable queries (1 CLS + 64 caption tokens) compressing variable-length sensor tokens to fixed-size representations
3. Text Encoder: First 12 layers of Gemma-3-270M (last 4 layers unfrozen during pretraining)
4. Multimodal Decoder: Last 6 layers of Gemma-3-270M extended with cross-attention for sensor-conditioned generation

Total: ~220M parameters, 67M trainable.

Results

Task	Metric	Score
Linear Probing (11 datasets avg.)	Accuracy	77.14%
Sensor-based QA	Accuracy	64.83%
Sensor Captioning	BERTScore	0.887

Linear probing accuracy represents a 5.93% relative improvement over baselines across 11 diverse datasets.

Checkpoints

File	Description
model.safetensors	Pretrained SLIP base model
har.safetensors	SFT for HAR chain-of-thought QA
sleep.safetensors	SFT for Sleep stage chain-of-thought QA
ecg.safetensors	SFT for ECG-QA chain-of-thought QA
tsqa.safetensors	SFT for time series QA
caption.safetensors	SFT for M4 sensor captioning

Installation

conda create -n slip python=3.10 -y && conda activate slip
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121
pip install -r requirement.txt

Download checkpoints:

from huggingface_hub import hf_hub_download

hf_hub_download("LeoChen085/SLIP", "SLIP_gemma270.pth", local_dir="ckpt")

# Optional: task-specific SFT checkpoints
for name in ["har", "sleep", "ecg", "tsqa", "caption"]:
    hf_hub_download("LeoChen085/SLIP", f"{name}.safetensors", local_dir="ckpt")

Quick Start

Load Model

from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("LeoChen085/SLIP", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("google/gemma-3-270m")
model.eval()

Get Contrastive Embeddings

import torch

device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device)

# Build sensor input (flexi-patch format)
batch_size, num_vars, num_patches, patch_size = 2, 3, 10, 16
sensor_ids, sensor_masks, sensor_times = [], [], []
for _ in range(batch_size):
    vars_x, vars_m, vars_t = [], [], []
    for _ in range(num_vars):
        vars_x.append(torch.randn(num_patches, patch_size, device=device))
        vars_m.append(torch.ones(num_patches, patch_size, device=device))
        vars_t.append(
            torch.linspace(0, 1, num_patches, device=device)
            .unsqueeze(-1).expand(num_patches, patch_size)
        )
    sensor_ids.append(vars_x)
    sensor_masks.append(vars_m)
    sensor_times.append(vars_t)

sensors = {
    "input_ids": sensor_ids,
    "attention_mask": sensor_masks,
    "time_index": sensor_times,
}

queries = ["Describe the pattern of this sensor data.", "What activity is this?"]
tok = tokenizer(queries, return_tensors="pt", padding=True, truncation=True, max_length=64)
text = {k: v.to(device) for k, v in tok.items()}

with torch.no_grad():
    text_emb, sensor_emb = model.get_embedding(text, sensors)

# text_emb / sensor_emb shape: (batch_size, 640)
sim = torch.nn.functional.cosine_similarity(text_emb, sensor_emb)
print(f"Cosine similarity: {sim.tolist()}")

Generate Text Conditioned on Sensor Data

prompt = "This sensor reading indicates"
gen_tok = tokenizer([prompt] * batch_size, return_tensors="pt", padding=True)
gen_text = {k: v.to(device) for k, v in gen_tok.items()}

with torch.no_grad():
    output_ids = model.generate(gen_text, sensors, max_new_tokens=50)

for i, ids in enumerate(output_ids):
    print(f"Sample {i}: {tokenizer.decode(ids, skip_special_tokens=True)}")

Get Sensor-Only Embeddings (No Text Needed)

with torch.no_grad():
    sensor_emb = model.get_sensor_embedding(
        input_ids=sensors["input_ids"],
        mask=sensors["attention_mask"],
        time_index=sensors["time_index"],
    )
# sensor_emb shape: (batch_size, 640)

Load Task-Specific SFT Checkpoint

from huggingface_hub import hf_hub_download
from safetensors.torch import load_file

har_path = hf_hub_download("LeoChen085/SLIP", "har.safetensors")
result = model.load_state_dict(load_file(har_path, device=str(device)), strict=False)
print(f"Loaded HAR checkpoint — missing: {len(result.missing_keys)}, unexpected: {len(result.unexpected_keys)}")

SFT Inference: Question Answering over Sensor Data

The SFT checkpoints enable natural-language Q&A directly on sensor signals. Each sample pairs a multivariate time series with a formatted prompt; the model generates a chain-of-thought reasoning trace followed by the final answer.

Input format (from the SFT dataset):

[sensor description / context]
Question: <question about the sensor data>
Answer:

The model continues from Answer: and produces the full response.

End-to-end inference example (using har_cot as an example task):

import torch
from transformers import AutoModel, AutoTokenizer
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
from torch.utils.data import DataLoader
from util.dataset import SftDataset, SFTCollator

device = "cuda" if torch.cuda.is_available() else "cpu"

# 1. Load base model and tokenizer
model = AutoModel.from_pretrained("LeoChen085/SLIP", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("google/gemma-3-270m")
model.eval().to(device)

# 2. Swap in the HAR SFT checkpoint
har_path = hf_hub_download("LeoChen085/SLIP", "har.safetensors")
model.load_state_dict(load_file(har_path, device=str(device)), strict=False)

# 3. Load SFT test data (auto-downloaded from HuggingFace)
test_set = SftDataset("har_cot", split="test", hf_repo="LeoChen085/SlipSFTDataset")
# is_test=True feeds only the prompt; answer is held out for evaluation
loader = DataLoader(test_set, batch_size=8,
                    collate_fn=SFTCollator(tokenizer, max_len=2880, is_test=True))

batch = next(iter(loader))
sensor = {k: (v.to(device) if torch.is_tensor(v) else v) for k, v in batch["sensor"].items()}
text   = {k: (v.to(device) if torch.is_tensor(v) else v) for k, v in batch["text"].items()}

# 4. Generate the answer
with torch.no_grad():
    output_ids = model.generate(text, sensor, max_new_tokens=200)

# Strip the prompt from the output — keep only the newly generated tokens
prompts      = tokenizer.batch_decode(text["input_ids"], skip_special_tokens=True)
answers      = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
ground_truths = text["labels"]   # list of strings when is_test=True

idx = 3
answer_only = answers[idx][len(prompts[idx]):].strip()

print("=== Model answer ===")
print(answer_only)
# The accelerometer data over the 2.56 second window shows relatively low
# variability and consistent patterns across the X, Y, and Z axes. The lack of
# large, rapid changes in acceleration across all axes suggests minimal physical
# activity, consistent with a stationary position. Answer: sitting.

print("\n=== Ground truth ===")
print(ground_truths[idx])
# The sustained low variability following the initial adjustment is characteristic
# of a sedentary behavior. Answer: sitting.

Available SFT tasks and their checkpoints:

Task	Checkpoint	Description
har_cot	har.safetensors	Human activity recognition with chain-of-thought (walking, running, cycling, …)
sleep_cot	sleep.safetensors	Sleep stage classification with CoT (Wake, N1, N2, N3, REM)
ecg_cot	ecg.safetensors	ECG morphology QA with CoT (normal/abnormal, rhythm, intervals)
tsqa	tsqa.safetensors	General time-series multiple-choice QA
m4_caption	caption.safetensors	Free-form natural-language captioning of M4 sensor traces

Replace "har_cot" / "har.safetensors" with any row from the table above to switch tasks.

Evaluation Datasets

The 11 evaluation datasets span four domains:

Domain	Datasets
Activity Recognition	WISDM, UCI-HAR
Clinical Diagnosis	Stroke (PPG_CVA), Diabetes (PPG_DM), Hypertension (PPG_HTN), Sleep Stage (sleepEDF), Heart Condition (ptbxl)
Stress Prediction	WESAD, StudentLife
Urban Sensing	AsphaltObstacles, Beijing AQI

Citation

@article{chen2026slip,
  title={Learning Transferable Sensor Models via Language-Informed Pretraining},
  author={Chen, Yuliang and Pillai, Arvind and Wu, Yu Yvonne and Griffin, Tess Z. and Marsch, Lisa and Heinz, Michael V. and Jacobson, Nicholas C. and Campbell, Andrew},
  journal={Preprint},
  year={2026}
}

License

This project is licensed under the MIT License.