layoutlmv3-finetuned-cord_100

README.md

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+---
+library_name: transformers
+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: 0.8836524300441826
+    - name: Recall
+      type: recall
+      value: 0.8982035928143712
+    - name: F1
+      type: f1
+      value: 0.8908685968819599
+    - name: Accuracy
+      type: accuracy
+      value: 0.9057724957555179
+---
+
+<!-- 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: 0.3809
+- Precision: 0.8837
+- Recall: 0.8982
+- F1: 0.8909
+- Accuracy: 0.9058
+
+## 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: 1
+- eval_batch_size: 1
+- 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        | 0.3125 | 250  | 1.4179          | 0.5786    | 0.6751 | 0.6231 | 0.7037   |
+| 1.8601        | 0.625  | 500  | 0.9021          | 0.7458    | 0.8016 | 0.7727 | 0.7988   |
+| 1.8601        | 0.9375 | 750  | 0.6900          | 0.8096    | 0.8338 | 0.8215 | 0.8294   |
+| 0.7675        | 1.25   | 1000 | 0.5915          | 0.8128    | 0.8481 | 0.8300 | 0.8544   |
+| 0.7675        | 1.5625 | 1250 | 0.5041          | 0.8381    | 0.8638 | 0.8507 | 0.8722   |
+| 0.4979        | 1.875  | 1500 | 0.4669          | 0.8413    | 0.8728 | 0.8567 | 0.8850   |
+| 0.4979        | 2.1875 | 1750 | 0.4080          | 0.8628    | 0.8847 | 0.8736 | 0.8990   |
+| 0.384         | 2.5    | 2000 | 0.3878          | 0.8731    | 0.8907 | 0.8818 | 0.9003   |
+| 0.384         | 2.8125 | 2250 | 0.3880          | 0.8794    | 0.8952 | 0.8872 | 0.9032   |
+| 0.3439        | 3.125  | 2500 | 0.3809          | 0.8837    | 0.8982 | 0.8909 | 0.9058   |
+
+
+### Framework versions
+
+- Transformers 4.45.1
+- Pytorch 2.4.1+cu121
+- Datasets 3.0.1
+- Tokenizers 0.20.0