update model card README.md

README.md

@@ -0,0 +1,96 @@
+---
+license: cc-by-nc-sa-4.0
+tags:
+- generated_from_trainer
+datasets:
+- cord
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: layoutlmv3-finetuned-cord_100
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: cord
+      type: cord
+      args: cord
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.9174649963154016
+    - name: Recall
+      type: recall
+      value: 0.9318862275449101
+    - name: F1
+      type: f1
+      value: 0.9246193835870776
+    - name: Accuracy
+      type: accuracy
+      value: 0.9405772495755518
+---
+
+<!-- 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 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2834
+- Precision: 0.9175
+- Recall: 0.9319
+- F1: 0.9246
+- Accuracy: 0.9406
+
+## 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  | 1.0175          | 0.7358    | 0.7882 | 0.7611 | 0.8014   |
+| 1.406         | 8.33  | 500  | 0.5646          | 0.8444    | 0.8735 | 0.8587 | 0.8671   |
+| 1.406         | 12.5  | 750  | 0.3943          | 0.8950    | 0.9184 | 0.9065 | 0.9189   |
+| 0.3467        | 16.67 | 1000 | 0.3379          | 0.9138    | 0.9289 | 0.9213 | 0.9291   |
+| 0.3467        | 20.83 | 1250 | 0.2842          | 0.9189    | 0.9334 | 0.9261 | 0.9419   |
+| 0.1484        | 25.0  | 1500 | 0.2822          | 0.9233    | 0.9371 | 0.9302 | 0.9427   |
+| 0.1484        | 29.17 | 1750 | 0.2906          | 0.9168    | 0.9319 | 0.9243 | 0.9372   |
+| 0.0825        | 33.33 | 2000 | 0.2922          | 0.9183    | 0.9334 | 0.9258 | 0.9410   |
+| 0.0825        | 37.5  | 2250 | 0.2842          | 0.9154    | 0.9319 | 0.9236 | 0.9397   |
+| 0.0596        | 41.67 | 2500 | 0.2834          | 0.9175    | 0.9319 | 0.9246 | 0.9406   |
+
+
+### Framework versions
+
+- Transformers 4.20.1
+- Pytorch 1.11.0
+- Datasets 2.1.0
+- Tokenizers 0.12.1