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library_name: peft
base_model: aubmindlab/bert-base-arabertv2
tags:
- text-classification
- sentiment-analysis
- arabic
- lora
- peft
- mlflow
language:
- ar
license: mit
pipeline_tag: text-classification
---
# arabic-sentiment-lora-deploy
**Deploy bundle for the [`arabic-sentiment-mlops`](https://github.com/psjprajna/arabic-sentiment-mlops)
LoRA sentiment classifier.** Not a standalone model card — this repo
holds the *artifacts* that the FastAPI serving container fetches at
build time. The model card lives on the [running Space](https://huggingface.co/spaces/PrajnaShetty/arabic-sentiment-lora).
**Live demo:** https://prajnashetty-arabic-sentiment-lora.hf.space — try
`GET /health` and `POST /predict` (Arabic text in; positive / negative /
neutral + confidence out).
## What's in this repo
| Path | Size | Purpose |
|-----------------------------------------------------------------------|---------|--------------------------------------------------------------------------------------------------------------------|
| `models/arabert-lora-v1/` | 2.8 MB | LoRA adapter weights + tokenizer + labels. Used as the filesystem fallback if registry resolution fails in-container. |
| `mlflow.db` | 892 KB | SQLite-backed MLflow registry. Holds the `arabert-lora` v1 registered model + its run lineage. |
| `mlruns/1/models/m-f9cac40424504a5a91d9449f38f5bd7c/` | 523 MB | MLflow 3.x logged-model artefacts — the pyfunc bundle (`MLmodel`, `python_model.pkl`, nested LoRA weights). |
Total: **27 files, ~525 MB**. LFS-tracked binaries handled by HF Hub
automatically.
## How it's consumed
The container's Dockerfile (cache-warm stage) fetches the whole repo
via `huggingface_hub.snapshot_download`:
```python
from huggingface_hub import snapshot_download
snapshot_download(
"PrajnaShetty/arabic-sentiment-lora-deploy",
repo_type="model",
local_dir="/opt/deploy",
)
```
The runtime stage `COPY`s `/opt/deploy` into `/app/`, applies a
build-time SQL rewrite over six columns in `mlflow.db` (the absolute
build-host paths get retargeted to `/app/…`), and ships a torch
compatibility shim that maps MPS-tagged tensor storage to CPU at
deserialize time (the LoRA was logged on Apple Silicon; the runtime
torch wheel is Linux CPU-only).
See [`Dockerfile`](https://github.com/psjprajna/arabic-sentiment-mlops/blob/main/Dockerfile)
and [ADR-0008](https://github.com/psjprajna/arabic-sentiment-mlops/blob/main/.claude/docs/adrs/0008-hf-hub-pull-on-build.md)
in the source repo for the full mechanics.
## Model card
The model itself is an [AraBERT v2](https://huggingface.co/aubmindlab/bert-base-arabertv2)
classifier fine-tuned with LoRA on the [HARD dataset](https://github.com/elnagara/HARD-Arabic-Dataset)
for 3-class sentiment (positive / negative / neutral). Macro F1 on the
held-out test split: **0.838** (positive 0.91, negative 0.85, neutral
0.76). Dialect breakdown (per Phase 5 in the source repo): **0.80
macro F1 on Gulf** vs **0.85 on MSA** — a ~5-point gap the repo
documents rather than hides.
Hardware constraints: trained on Apple Silicon MPS (~80 min wall);
served on CPU at ~600 ms per `/predict` call on HF Spaces' free tier
(2 vCPU / 16 GB RAM).
## Loading the LoRA outside the Space
If you want to use just the LoRA adapter without the FastAPI container:
```python
from peft import PeftModel
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from huggingface_hub import snapshot_download
bundle = snapshot_download(
"PrajnaShetty/arabic-sentiment-lora-deploy",
repo_type="model",
allow_patterns="models/arabert-lora-v1/*",
)
base = AutoModelForSequenceClassification.from_pretrained(
"aubmindlab/bert-base-arabertv2", num_labels=3
)
model = PeftModel.from_pretrained(base, f"{bundle}/models/arabert-lora-v1")
tokenizer = AutoTokenizer.from_pretrained(f"{bundle}/models/arabert-lora-v1")
```
## Source
- **App code + Dockerfile + ADRs:** https://github.com/psjprajna/arabic-sentiment-mlops
- **Running Space:** https://huggingface.co/spaces/PrajnaShetty/arabic-sentiment-lora
- **Live API:** https://prajnashetty-arabic-sentiment-lora.hf.space
## License
MIT. The base model ([`aubmindlab/bert-base-arabertv2`](https://huggingface.co/aubmindlab/bert-base-arabertv2))
and the [HARD dataset](https://github.com/elnagara/HARD-Arabic-Dataset)
are under their own licenses; please check there if you intend to use
this bundle outside personal / academic contexts.
|