layoutlmv3-finetuned-cord_100

README.md

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+---
+license: cc-by-nc-sa-4.0
+base_model: microsoft/layoutlmv3-base
+tags:
+- generated_from_trainer
+datasets:
+- cord-layoutlmv3
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: layoutlmv3-finetuned-cord_100
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: cord-layoutlmv3
+      type: cord-layoutlmv3
+      config: cord
+      split: test
+      args: cord
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.9266666666666666
+    - name: Recall
+      type: recall
+      value: 0.936377245508982
+    - name: F1
+      type: f1
+      value: 0.9314966492926285
+    - name: Accuracy
+      type: accuracy
+      value: 0.9354838709677419
+---
+
+<!-- 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-finetuned-cord_100
+
+This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the cord-layoutlmv3 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3194
+- Precision: 0.9267
+- Recall: 0.9364
+- F1: 0.9315
+- Accuracy: 0.9355
+
+## 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: 5
+- eval_batch_size: 5
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- training_steps: 2500
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 4.17  | 250  | 1.0054          | 0.7555    | 0.8024 | 0.7782 | 0.8081   |
+| 1.4019        | 8.33  | 500  | 0.5287          | 0.8320    | 0.8638 | 0.8476 | 0.8739   |
+| 1.4019        | 12.5  | 750  | 0.3790          | 0.9043    | 0.9192 | 0.9117 | 0.9236   |
+| 0.3185        | 16.67 | 1000 | 0.3253          | 0.9178    | 0.9281 | 0.9230 | 0.9355   |
+| 0.3185        | 20.83 | 1250 | 0.3231          | 0.9223    | 0.9334 | 0.9278 | 0.9304   |
+| 0.1319        | 25.0  | 1500 | 0.3039          | 0.9317    | 0.9394 | 0.9355 | 0.9419   |
+| 0.1319        | 29.17 | 1750 | 0.3142          | 0.9287    | 0.9364 | 0.9325 | 0.9334   |
+| 0.0725        | 33.33 | 2000 | 0.2982          | 0.9296    | 0.9386 | 0.9341 | 0.9419   |
+| 0.0725        | 37.5  | 2250 | 0.3189          | 0.9288    | 0.9371 | 0.9329 | 0.9346   |
+| 0.0549        | 41.67 | 2500 | 0.3194          | 0.9267    | 0.9364 | 0.9315 | 0.9355   |
+
+
+### Framework versions
+
+- Transformers 4.36.2
+- Pytorch 2.1.0+cu121
+- Datasets 2.16.1
+- Tokenizers 0.15.0

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