layoutlmv3-finetuned-cord_5

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: 0.596078431372549
+    - name: Recall
+      type: recall
+      value: 0.6826347305389222
+    - name: F1
+      type: f1
+      value: 0.636427076064201
+    - name: Accuracy
+      type: accuracy
+      value: 0.684634974533107
+---
+
+<!-- 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: 1.9357
+- Precision: 0.5961
+- Recall: 0.6826
+- F1: 0.6364
+- Accuracy: 0.6846
+
+## 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        | 250.0  | 250  | 1.5298          | 0.5778    | 0.6781 | 0.6240 | 0.6825   |
+| 0.6654        | 500.0  | 500  | 1.6175          | 0.5942    | 0.6849 | 0.6363 | 0.6880   |
+| 0.6654        | 750.0  | 750  | 1.7087          | 0.5947    | 0.6841 | 0.6363 | 0.6876   |
+| 0.0208        | 1000.0 | 1000 | 1.7729          | 0.5948    | 0.6834 | 0.6360 | 0.6859   |
+| 0.0208        | 1250.0 | 1250 | 1.8273          | 0.5949    | 0.6826 | 0.6358 | 0.6851   |
+| 0.0099        | 1500.0 | 1500 | 1.8693          | 0.5957    | 0.6826 | 0.6362 | 0.6846   |
+| 0.0099        | 1750.0 | 1750 | 1.8969          | 0.5950    | 0.6819 | 0.6355 | 0.6842   |
+| 0.0066        | 2000.0 | 2000 | 1.9196          | 0.5972    | 0.6826 | 0.6371 | 0.6842   |
+| 0.0066        | 2250.0 | 2250 | 1.9312          | 0.5946    | 0.6819 | 0.6353 | 0.6838   |
+| 0.0054        | 2500.0 | 2500 | 1.9357          | 0.5961    | 0.6826 | 0.6364 | 0.6846   |
+
+
+### Framework versions
+
+- Transformers 4.34.0
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.5
+- Tokenizers 0.14.1

pytorch_model.bin

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