File size: 3,883 Bytes
786cc60 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 | ---
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: text-classification
base_model: distilbert/distilbert-base-uncased
datasets:
- OniReimu/Edge-Computing-JEV
tags:
- edge-computing
- service-orchestration
- intent-classification
---
# Edge-Computing-JEV service classifiers
Four DistilBERT service classifiers used as reference interpreters in RQ4 (dynamic service catalog) of the paper
> **Replacing Large Language Models with Jev Decision Models for Low-Latency Edge Service Orchestration**
> Delong Li, Xu Wang, Haochen Gong, Rui Lang, and Guangsheng Yu. University of Technology Sydney.
Each classifier maps a natural-language edge-service request to one service of a fixed catalog (or `unsupported`).
They show what a trained classifier needs when the catalog changes, in contrast to decision models and LLMs that
receive the catalog with each request.
Code: [github.com/OniReimu/Edge-Computing-JEV](https://github.com/OniReimu/Edge-Computing-JEV) ·
Benchmark and run records: [datasets/OniReimu/Edge-Computing-JEV](https://huggingface.co/datasets/OniReimu/Edge-Computing-JEV)
## Models
Every classifier is trained from `distilbert/distilbert-base-uncased` at revision `12040accade4e8a0f71eabdb258fecc2e7e948be`.
| Folder | Paper name | Labels | Training examples | Used for |
|---|---|---|---|---|
| `clf_all` | DistilBERT-Clf-All | 255 (254 services + `unsupported`) | 554: one description per service + 300 development cases of the catalog-size conditions | RQ4 catalog-size conditions (K = 4 to 254) |
| `clf_frozen` | DistilBERT-Clf-Frozen | 65 (64 services of catalog v0 + `unsupported`) | 322: one description per service + 258 development cases | RQ4 churn conditions, without adaptation |
| `clf_retrained_25` | DistilBERT-Clf-Retrained (25% churn) | 65 (catalog v1) | 339, of which 76 are new labelled examples (16 descriptions of new services + 60 churn development cases) | RQ4 25% churn |
| `clf_retrained_50` | DistilBERT-Clf-Retrained (50% churn) | 65 (catalog v2) | 278, of which 92 are new labelled examples (32 descriptions of new services + 60 churn development cases) | RQ4 50% churn |
Training examples come only from the EdgeIntent v1 development split and the catalog descriptions; no test case is
used. Hyperparameters are fixed, with no search: max length 128, learning rate 5e-5, batch size 16, 10 epochs,
weight decay 0.01, seed 20260924, trained on Apple M4 Max (MPS). Each folder's `training.json` records the label space,
example counts, and training wall time. `scripts/eb_rq4_train.py` in the GitHub repository rebuilds all four.
## Results on the churn conditions
Service top-1 accuracy on the EdgeIntent v1 test split, from `experiments/rq1-rq4-interpretation/results/h5_classifier_reference.csv`:
| Condition | Classifier | Seen services | Unseen services |
|---|---|---|---|
| 25% churn | Frozen | 0.653 | 0.000 |
| 25% churn | Retrained | 0.708 | 0.147 |
| 50% churn | Frozen | 0.522 | 0.000 |
| 50% churn | Retrained | 0.441 | 0.142 |
Results on the catalog-size conditions are in `experiments/rq1-rq4-interpretation/results/cells.csv`
(model `DistilBERT-Clf-All`).
## Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
repo = "OniReimu/Edge-Computing-JEV-classifiers"
tok = AutoTokenizer.from_pretrained(repo, subfolder="clf_all")
model = AutoModelForSequenceClassification.from_pretrained(repo, subfolder="clf_all")
inputs = tok("Please read the licence plate on the gate camera frame, keep it on site.",
return_tensors="pt", truncation=True, max_length=128)
print(model.config.id2label[model(**inputs).logits.argmax(-1).item()])
```
## Limitations
These are reference baselines trained with a small, fixed recipe on synthetic requests. They are not tuned and are not
intended for deployment.
## License
Apache-2.0, as the base model.
|