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
File size: 857 Bytes
0282c5b | 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 | {
"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"
} |