README.md

---
license: gemma
base_model: unsloth/gemma-2-2b-it
library_name: peft
tags: [hebrew, lora, qlora, information-extraction, israeli-tax, text2receipt]
language: [he]
---

# Text2Receipt — Parser (LoRA adapter)

LoRA adapter over `unsloth/gemma-2-2b-it` that extracts a structured `parse` from a messy free-text **Hebrew**
income note. Part of the [Text2Receipt](https://huggingface.co/datasets/yonilev/Text2Receipt) project. The model
predicts **only** the linguistically-present fields (`client_name`, `client_is_business`, `items`); all fiscal
arithmetic (VAT, totals, allocation number, serials) is handled deterministically by `complete()`.

## Recommendation encoder (bake-off)

Three multilingual encoders were scored on Recall@k for same-category retrieval; the winner powers the
"similar past receipts" feature in the Space.

| model                                                       |   params_M |   dim |   encode_sec |   recall@1 |   recall@3 |   recall@5 |
|:------------------------------------------------------------|-----------:|------:|-------------:|-----------:|-----------:|-----------:|
| intfloat/multilingual-e5-small                              |      117.7 |   384 |         15   |     0.9675 |     0.934  |     0.9114 |
| sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |      117.7 |   384 |         17.4 |     0.909  |     0.872  |     0.8484 |
| sentence-transformers/distiluse-base-multilingual-cased-v2  |      134.7 |   512 |         19.5 |     0.8765 |     0.8338 |     0.8083 |

**Winner:** `intfloat/multilingual-e5-small`

## Fine-tune vs baseline

Parse-extraction quality, baseline (zero-shot) vs LoRA fine-tune, across the in-distribution test, the
disjoint-vocabulary OOD test, and the hand-written human test:

| model                | split      |   valid_json |   exact_match |   field_f1 |
|:---------------------|:-----------|-------------:|--------------:|-----------:|
| baseline (zero-shot) | iid_test   |         0.85 |         0.23  |     0.6093 |
| baseline (zero-shot) | ood_test   |         0.9  |         0.17  |     0.6233 |
| baseline (zero-shot) | human_test |         0.8  |         0.08  |     0.5066 |
| fine-tuned (LoRA)    | iid_test   |         1    |         0.915 |     0.9735 |
| fine-tuned (LoRA)    | ood_test   |         1    |         0.325 |     0.8382 |
| fine-tuned (LoRA)    | human_test |         1    |         0.08  |     0.73   |

![bake-off](assets/embedding_bakeoff.png)
![fine-tune](assets/finetune_comparison.png)

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

base = "unsloth/gemma-2-2b-it"
tok = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base, device_map="auto", torch_dtype=torch.float16)
model = PeftModel.from_pretrained(model, "yonilev/Text2Receipt-parser")
```

Adapter + embeddings artifacts (`receipts_store.parquet`, `receipts_embeddings.npy`,
`embeddings_manifest.json`) are in this repo for the application to consume.