End of training

README.md

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+---
+license: mit
+base_model: SCUT-DLVCLab/lilt-roberta-en-base
+tags:
+- generated_from_trainer
+model-index:
+- name: lilt-roBERTa-en-base-sroie
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/miss1dua/huggingface/runs/r5thm7ml)
+# lilt-roBERTa-en-base-sroie
+
+This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0348
+- Address: {'precision': 0.92, 'recall': 0.9279538904899135, 'f1': 0.9239598278335724, 'number': 347}
+- Company: {'precision': 0.9405099150141643, 'recall': 0.9567723342939481, 'f1': 0.9485714285714285, 'number': 347}
+- Date: {'precision': 0.9827586206896551, 'recall': 0.9855907780979827, 'f1': 0.9841726618705036, 'number': 347}
+- Total: {'precision': 0.9131652661064426, 'recall': 0.9394812680115274, 'f1': 0.9261363636363636, 'number': 347}
+- Overall Precision: 0.9389
+- Overall Recall: 0.9524
+- Overall F1: 0.9456
+- Overall Accuracy: 0.9954
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 3e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 20
+
+### Training results
+
+| Training Loss | Epoch   | Step | Validation Loss | Address                                                                                                  | Company                                                                                                  | Date                                                                                                     | Total                                                                                                    | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.0536        | 6.3291  | 500  | 0.0261          | {'precision': 0.9067796610169492, 'recall': 0.9250720461095101, 'f1': 0.9158345221112697, 'number': 347} | {'precision': 0.9273743016759777, 'recall': 0.9567723342939481, 'f1': 0.9418439716312057, 'number': 347} | {'precision': 0.9828080229226361, 'recall': 0.9884726224783862, 'f1': 0.985632183908046, 'number': 347}  | {'precision': 0.883008356545961, 'recall': 0.9135446685878963, 'f1': 0.8980169971671388, 'number': 347}  | 0.9246            | 0.9460         | 0.9352     | 0.9949           |
+| 0.0058        | 12.6582 | 1000 | 0.0326          | {'precision': 0.9176136363636364, 'recall': 0.930835734870317, 'f1': 0.9241773962804005, 'number': 347}  | {'precision': 0.9323943661971831, 'recall': 0.9538904899135446, 'f1': 0.9430199430199431, 'number': 347} | {'precision': 0.9827586206896551, 'recall': 0.9855907780979827, 'f1': 0.9841726618705036, 'number': 347} | {'precision': 0.8910081743869209, 'recall': 0.9423631123919308, 'f1': 0.9159663865546217, 'number': 347} | 0.9304            | 0.9532         | 0.9416     | 0.9950           |
+| 0.0019        | 18.9873 | 1500 | 0.0348          | {'precision': 0.92, 'recall': 0.9279538904899135, 'f1': 0.9239598278335724, 'number': 347}               | {'precision': 0.9405099150141643, 'recall': 0.9567723342939481, 'f1': 0.9485714285714285, 'number': 347} | {'precision': 0.9827586206896551, 'recall': 0.9855907780979827, 'f1': 0.9841726618705036, 'number': 347} | {'precision': 0.9131652661064426, 'recall': 0.9394812680115274, 'f1': 0.9261363636363636, 'number': 347} | 0.9389            | 0.9524         | 0.9456     | 0.9954           |
+
+
+### Framework versions
+
+- Transformers 4.42.3
+- Pytorch 2.1.2
+- Datasets 2.20.0
+- Tokenizers 0.19.1

model.safetensors

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preprocessor_config.json

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+{
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+  "do_normalize": true,
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+  "do_resize": true,
+  "image_mean": [
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+  "image_processor_type": "LayoutLMv3FeatureExtractor",
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+  "processor_class": "LayoutLMv3Processor",
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+  "size": {
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+    "width": 224
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+  "tesseract_config": ""
+}

special_tokens_map.json

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+}

tokenizer_config.json

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+  "errors": "replace",
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+}