layoutlmv3-finetuned-cord_100

README.md

@@ -0,0 +1,99 @@
+---
+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: 0.9243884358784284
+    - name: Recall
+      type: recall
+      value: 0.9333832335329342
+    - name: F1
+      type: f1
+      value: 0.9288640595903166
+    - name: Accuracy
+      type: accuracy
+      value: 0.9363327674023769
+---
+
+<!-- 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: 0.3467
+- Precision: 0.9244
+- Recall: 0.9334
+- F1: 0.9289
+- Accuracy: 0.9363
+
+## 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  | 0.5174          | 0.8469    | 0.8735 | 0.8600 | 0.8790   |
+| 0.5511        | 8.33  | 500  | 0.3975          | 0.8999    | 0.9147 | 0.9072 | 0.9194   |
+| 0.5511        | 12.5  | 750  | 0.3872          | 0.9015    | 0.9184 | 0.9099 | 0.9189   |
+| 0.1802        | 16.67 | 1000 | 0.3416          | 0.9180    | 0.9296 | 0.9238 | 0.9338   |
+| 0.1802        | 20.83 | 1250 | 0.3311          | 0.9159    | 0.9289 | 0.9223 | 0.9359   |
+| 0.0836        | 25.0  | 1500 | 0.3457          | 0.9192    | 0.9281 | 0.9236 | 0.9334   |
+| 0.0836        | 29.17 | 1750 | 0.3347          | 0.9202    | 0.9319 | 0.9260 | 0.9291   |
+| 0.0473        | 33.33 | 2000 | 0.3677          | 0.9194    | 0.9304 | 0.9249 | 0.9253   |
+| 0.0473        | 37.5  | 2250 | 0.3433          | 0.9279    | 0.9341 | 0.9310 | 0.9376   |
+| 0.0342        | 41.67 | 2500 | 0.3467          | 0.9244    | 0.9334 | 0.9289 | 0.9363   |
+
+
+### Framework versions
+
+- Transformers 4.32.1
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.4
+- Tokenizers 0.13.3