update model card README.md

README.md

@@ -0,0 +1,98 @@
+---
+license: cc-by-nc-sa-4.0
+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: train
+      args: cord
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.9415247964470762
+    - name: Recall
+      type: recall
+      value: 0.9520958083832335
+    - name: F1
+      type: f1
+      value: 0.9467807964272422
+    - name: Accuracy
+      type: accuracy
+      value: 0.9575551782682513
+---
+
+<!-- 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: 0.2246
+- Precision: 0.9415
+- Recall: 0.9521
+- F1: 0.9468
+- Accuracy: 0.9576
+
+## 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  | 1.0265          | 0.7630    | 0.8099 | 0.7858 | 0.8086   |
+| 1.4021        | 3.12  | 500  | 0.5804          | 0.8290    | 0.8638 | 0.8460 | 0.8718   |
+| 1.4021        | 4.69  | 750  | 0.3937          | 0.8882    | 0.9034 | 0.8957 | 0.9126   |
+| 0.4062        | 6.25  | 1000 | 0.3171          | 0.9137    | 0.9274 | 0.9205 | 0.9351   |
+| 0.4062        | 7.81  | 1250 | 0.2798          | 0.9332    | 0.9409 | 0.9370 | 0.9444   |
+| 0.2212        | 9.38  | 1500 | 0.2558          | 0.9277    | 0.9416 | 0.9346 | 0.9461   |
+| 0.2212        | 10.94 | 1750 | 0.2479          | 0.9335    | 0.9454 | 0.9394 | 0.9516   |
+| 0.1525        | 12.5  | 2000 | 0.2356          | 0.9444    | 0.9536 | 0.9490 | 0.9588   |
+| 0.1525        | 14.06 | 2250 | 0.2286          | 0.9365    | 0.9491 | 0.9428 | 0.9563   |
+| 0.1134        | 15.62 | 2500 | 0.2246          | 0.9415    | 0.9521 | 0.9468 | 0.9576   |
+
+
+### Framework versions
+
+- Transformers 4.25.1
+- Pytorch 1.13.0+cu116
+- Datasets 2.8.0
+- Tokenizers 0.13.2