Instructions to use yonilev/Text2Receipt-parser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use yonilev/Text2Receipt-parser with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-2-2b-it") model = PeftModel.from_pretrained(base_model, "yonilev/Text2Receipt-parser") - Notebooks
- Google Colab
- Kaggle
| { | |
| "embed_model": "intfloat/multilingual-e5-small", | |
| "dim": 384, | |
| "n_vectors": 10025, | |
| "e5_family": true, | |
| "bakeoff": [ | |
| { | |
| "model": "intfloat/multilingual-e5-small", | |
| "params_M": 117.7, | |
| "dim": 384, | |
| "encode_sec": 15.0, | |
| "recall@1": 0.9675, | |
| "recall@3": 0.934, | |
| "recall@5": 0.9114 | |
| }, | |
| { | |
| "model": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2", | |
| "params_M": 117.7, | |
| "dim": 384, | |
| "encode_sec": 17.4, | |
| "recall@1": 0.909, | |
| "recall@3": 0.872, | |
| "recall@5": 0.8484 | |
| }, | |
| { | |
| "model": "sentence-transformers/distiluse-base-multilingual-cased-v2", | |
| "params_M": 134.7, | |
| "dim": 512, | |
| "encode_sec": 19.5, | |
| "recall@1": 0.8765, | |
| "recall@3": 0.8338, | |
| "recall@5": 0.8083 | |
| } | |
| ], | |
| "built_at": "2026-06-23T11:38:06.847021+00:00" | |
| } |