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donut-receipt-lora-testing-adapter

Document Question Answering
PEFT
Safetensors
English
lora
donut
receipt-extraction
Model card Files
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Community
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Upload LoRA adapter with valid metadata

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Files changed (9) hide show
  1. README.md +30 -0
  2. adapter_config.json +33 -0
  3. adapter_model.safetensors +3 -0
  4. added_tokens.json +34 -0
  5. preprocessor_config.json +26 -0
  6. sentencepiece.bpe.model +3 -0
  7. special_tokens_map.json +69 -0
  8. tokenizer.json +0 -0
  9. tokenizer_config.json +343 -0
README.md ADDED
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+ ---
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+ language: en
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+ base_model: Bennet1996/donut-small
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+ tags:
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+ - lora
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+ - donut
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+ - receipt-extraction
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+ library_name: peft
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+ license: apache-2.0
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+ datasets:
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+ - receipt-dataset
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+ pipeline_tag: document-question-answering
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+ ---
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+
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+ # Donut Receipt LoRA Adapter 🧾
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+
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+ This is a **LoRA adapter** fine-tuned on `Bennet1996/donut-small` for receipt key information extraction.
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+
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+ **Extracts:**
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+ - `company`
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+ - `date` (any format: `DD/MM/YYYY`, `DD-MM-YYYY`, etc.)
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+ - `address`
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+ - `total`
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+
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+ You can attach this adapter to the base Donut model like:
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+ ```python
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+ from peft import PeftModel
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+ from transformers import VisionEncoderDecoderModel
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+ base = VisionEncoderDecoderModel.from_pretrained("Bennet1996/donut-small")
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+ model = PeftModel.from_pretrained(base, "YOUR_USERNAME/donut-receipt-lora-finetuned-adapter")
adapter_config.json ADDED
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+ "bias": "none",
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+ "inference_mode": true,
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+ "init_lora_weights": true,
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+ "layer_replication": null,
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+ "layers_pattern": null,
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+ "layers_to_transform": null,
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+ "loftq_config": {},
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+ "lora_alpha": 32,
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+ "lora_dropout": 0.05,
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+ "megatron_config": null,
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+ "megatron_core": "megatron.core",
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+ "modules_to_save": null,
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+ "peft_type": "LORA",
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+ "r": 16,
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+ "rank_pattern": {},
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+ "revision": null,
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+ "target_modules": [
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+ "fc2",
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+ "q_proj",
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+ "fc1",
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+ "out_proj",
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+ "k_proj",
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+ "v_proj"
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+ ],
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+ "task_type": "CAUSAL_LM",
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+ "use_dora": false,
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+ "use_rslora": false
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+ }
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