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
Upload embeddings_manifest.json with huggingface_hub
Browse files- embeddings_manifest.json +36 -0
embeddings_manifest.json
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{
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"embed_model": "intfloat/multilingual-e5-small",
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"dim": 384,
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"n_vectors": 10025,
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"e5_family": true,
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"bakeoff": [
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{
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"model": "intfloat/multilingual-e5-small",
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"params_M": 117.7,
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"dim": 384,
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"encode_sec": 15.0,
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"recall@1": 0.9675,
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"recall@3": 0.934,
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"recall@5": 0.9114
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},
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{
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"model": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
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"params_M": 117.7,
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"dim": 384,
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"encode_sec": 17.4,
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"recall@1": 0.909,
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"recall@3": 0.872,
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"recall@5": 0.8484
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},
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{
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"model": "sentence-transformers/distiluse-base-multilingual-cased-v2",
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"params_M": 134.7,
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"dim": 512,
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"encode_sec": 19.5,
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"recall@1": 0.8765,
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"recall@3": 0.8338,
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"recall@5": 0.8083
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}
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],
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"built_at": "2026-06-23T11:38:06.847021+00:00"
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}
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