update model card README.md

README.md

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+---
+license: cc-by-nc-sa-4.0
+tags:
+- generated_from_trainer
+datasets:
+- dataset
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: sougemi_model
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: dataset
+      type: dataset
+      config: discharge
+      split: test
+      args: discharge
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.845360824742268
+    - name: Recall
+      type: recall
+      value: 0.8913043478260869
+    - name: F1
+      type: f1
+      value: 0.8677248677248677
+    - name: Accuracy
+      type: accuracy
+      value: 0.9533678756476683
+---
+
+<!-- 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. -->
+
+# sougemi_model
+
+This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the dataset dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1812
+- Precision: 0.8454
+- Recall: 0.8913
+- F1: 0.8677
+- Accuracy: 0.9534
+
+## 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: 2
+- eval_batch_size: 2
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- training_steps: 1000
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 33.33  | 100  | 0.7803          | 0.8966    | 0.8478 | 0.8715 | 0.9663   |
+| No log        | 66.67  | 200  | 0.3016          | 0.8696    | 0.8696 | 0.8696 | 0.9767   |
+| No log        | 100.0  | 300  | 0.1623          | 0.9130    | 0.9130 | 0.9130 | 0.9819   |
+| No log        | 133.33 | 400  | 0.1680          | 0.8454    | 0.8913 | 0.8677 | 0.9637   |
+| 0.5801        | 166.67 | 500  | 0.1812          | 0.8454    | 0.8913 | 0.8677 | 0.9534   |
+| 0.5801        | 200.0  | 600  | 0.1231          | 0.8947    | 0.9239 | 0.9091 | 0.9715   |
+| 0.5801        | 233.33 | 700  | 0.1363          | 0.8617    | 0.8804 | 0.8710 | 0.9663   |
+| 0.5801        | 266.67 | 800  | 0.1949          | 0.8333    | 0.8696 | 0.8511 | 0.9508   |
+| 0.5801        | 300.0  | 900  | 0.1749          | 0.8163    | 0.8696 | 0.8421 | 0.9534   |
+| 0.0607        | 333.33 | 1000 | 0.1817          | 0.8163    | 0.8696 | 0.8421 | 0.9534   |
+
+
+### Framework versions
+
+- Transformers 4.26.0.dev0
+- Pytorch 1.13.0+cu116
+- Datasets 2.8.0
+- Tokenizers 0.13.2