layoutlmv3-invoice

README.md

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+---
+license: cc-by-nc-sa-4.0
+base_model: microsoft/layoutlmv3-base
+tags:
+- generated_from_trainer
+datasets:
+- layoutlmv3
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: layoutlmv3-invoice
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: layoutlmv3
+      type: layoutlmv3
+      config: InvoiceExtraction
+      split: test
+      args: InvoiceExtraction
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.9698412698412698
+    - name: Recall
+      type: recall
+      value: 0.9591836734693877
+    - name: F1
+      type: f1
+      value: 0.9644830307813733
+    - name: Accuracy
+      type: accuracy
+      value: 0.9708383961117861
+---
+
+<!-- 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. -->
+
+# layoutlmv3-invoice
+
+This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the layoutlmv3 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1889
+- Precision: 0.9698
+- Recall: 0.9592
+- F1: 0.9645
+- Accuracy: 0.9708
+
+## 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: 1e-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
+- training_steps: 3500
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 5.32  | 250  | 0.3826          | 0.9471    | 0.9278 | 0.9374 | 0.9465   |
+| 0.8433        | 10.64 | 500  | 0.1720          | 0.9697    | 0.9560 | 0.9628 | 0.9684   |
+| 0.8433        | 15.96 | 750  | 0.1631          | 0.9714    | 0.9608 | 0.9661 | 0.9684   |
+| 0.0347        | 21.28 | 1000 | 0.1548          | 0.9746    | 0.9639 | 0.9692 | 0.9733   |
+| 0.0347        | 26.6  | 1250 | 0.1700          | 0.9698    | 0.9576 | 0.9637 | 0.9672   |
+| 0.0116        | 31.91 | 1500 | 0.1812          | 0.9667    | 0.9576 | 0.9621 | 0.9648   |
+| 0.0116        | 37.23 | 1750 | 0.1513          | 0.9683    | 0.9592 | 0.9637 | 0.9721   |
+| 0.0066        | 42.55 | 2000 | 0.1555          | 0.9730    | 0.9623 | 0.9676 | 0.9757   |
+| 0.0066        | 47.87 | 2250 | 0.1729          | 0.9714    | 0.9592 | 0.9652 | 0.9708   |
+| 0.0048        | 53.19 | 2500 | 0.1854          | 0.9761    | 0.9623 | 0.9692 | 0.9721   |
+| 0.0048        | 58.51 | 2750 | 0.1863          | 0.9714    | 0.9592 | 0.9652 | 0.9696   |
+| 0.0037        | 63.83 | 3000 | 0.1813          | 0.9761    | 0.9623 | 0.9692 | 0.9733   |
+| 0.0037        | 69.15 | 3250 | 0.1903          | 0.9698    | 0.9592 | 0.9645 | 0.9708   |
+| 0.0034        | 74.47 | 3500 | 0.1889          | 0.9698    | 0.9592 | 0.9645 | 0.9708   |
+
+
+### Framework versions
+
+- Transformers 4.36.0.dev0
+- Pytorch 2.1.0+cu118
+- Datasets 2.15.0
+- Tokenizers 0.15.0

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