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: 0.9458054936896808
+    - name: Recall
+      type: recall
+      value: 0.9535928143712575
+    - name: F1
+      type: f1
+      value: 0.9496831904584422
+    - name: Accuracy
+      type: accuracy
+      value: 0.9588285229202037
+---
+
+<!-- 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: 0.2033
+- Precision: 0.9458
+- Recall: 0.9536
+- F1: 0.9497
+- Accuracy: 0.9588
+
+## 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        | 1.56  | 250  | 1.0015          | 0.7227    | 0.7822 | 0.7513 | 0.7963   |
+| 1.3862        | 3.12  | 500  | 0.5334          | 0.8591    | 0.8765 | 0.8677 | 0.8837   |
+| 1.3862        | 4.69  | 750  | 0.3689          | 0.8925    | 0.9072 | 0.8998 | 0.9164   |
+| 0.3835        | 6.25  | 1000 | 0.2877          | 0.9281    | 0.9371 | 0.9326 | 0.9431   |
+| 0.3835        | 7.81  | 1250 | 0.2506          | 0.9312    | 0.9424 | 0.9368 | 0.9452   |
+| 0.2048        | 9.38  | 1500 | 0.2373          | 0.9480    | 0.9543 | 0.9511 | 0.9554   |
+| 0.2048        | 10.94 | 1750 | 0.2184          | 0.9379    | 0.9491 | 0.9435 | 0.9542   |
+| 0.1365        | 12.5  | 2000 | 0.2057          | 0.9393    | 0.9506 | 0.9449 | 0.9567   |
+| 0.1365        | 14.06 | 2250 | 0.2024          | 0.9487    | 0.9543 | 0.9515 | 0.9576   |
+| 0.1067        | 15.62 | 2500 | 0.2033          | 0.9458    | 0.9536 | 0.9497 | 0.9588   |
+
+
+### Framework versions
+
+- Transformers 4.35.0
+- Pytorch 2.1.0+cu118
+- Datasets 2.14.6
+- Tokenizers 0.14.1