End of training

README.md

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+---
+license: cc-by-nc-sa-4.0
+base_model: microsoft/layoutlmv3-base
+tags:
+- generated_from_trainer
+datasets:
+- my_csv_dataset3
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: pharma_label_v3.1
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: my_csv_dataset3
+      type: my_csv_dataset3
+      config: discharge
+      split: test
+      args: discharge
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.9623287671232876
+    - name: Recall
+      type: recall
+      value: 0.9740034662045061
+    - name: F1
+      type: f1
+      value: 0.9681309216192937
+    - name: Accuracy
+      type: accuracy
+      value: 0.9890616004605642
+---
+
+<!-- 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. -->
+
+# pharma_label_v3.1
+
+This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the my_csv_dataset3 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0671
+- Precision: 0.9623
+- Recall: 0.9740
+- F1: 0.9681
+- Accuracy: 0.9891
+
+## 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: 1500
+
+### Training results
+
+| Training Loss | Epoch   | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.2987  | 100  | 0.5492          | 0.7759    | 0.7140 | 0.7437 | 0.9102   |
+| No log        | 2.5974  | 200  | 0.1522          | 0.9281    | 0.9393 | 0.9337 | 0.9747   |
+| No log        | 3.8961  | 300  | 0.1063          | 0.9332    | 0.9445 | 0.9388 | 0.9793   |
+| No log        | 5.1948  | 400  | 0.0891          | 0.9448    | 0.9497 | 0.9473 | 0.9810   |
+| 0.375         | 6.4935  | 500  | 0.0879          | 0.9435    | 0.9549 | 0.9492 | 0.9839   |
+| 0.375         | 7.7922  | 600  | 0.0908          | 0.9485    | 0.9584 | 0.9534 | 0.9822   |
+| 0.375         | 9.0909  | 700  | 0.0764          | 0.9636    | 0.9636 | 0.9636 | 0.9862   |
+| 0.375         | 10.3896 | 800  | 0.0819          | 0.9671    | 0.9671 | 0.9671 | 0.9873   |
+| 0.375         | 11.6883 | 900  | 0.0802          | 0.9686    | 0.9636 | 0.9661 | 0.9873   |
+| 0.0225        | 12.9870 | 1000 | 0.0602          | 0.9722    | 0.9705 | 0.9714 | 0.9902   |
+| 0.0225        | 14.2857 | 1100 | 0.0989          | 0.9438    | 0.9601 | 0.9519 | 0.9816   |
+| 0.0225        | 15.5844 | 1200 | 0.0859          | 0.9538    | 0.9671 | 0.9604 | 0.9839   |
+| 0.0225        | 16.8831 | 1300 | 0.0781          | 0.9554    | 0.9653 | 0.9603 | 0.9856   |
+| 0.0225        | 18.1818 | 1400 | 0.0653          | 0.9605    | 0.9705 | 0.9655 | 0.9891   |
+| 0.0105        | 19.4805 | 1500 | 0.0671          | 0.9623    | 0.9740 | 0.9681 | 0.9891   |
+
+
+### Framework versions
+
+- Transformers 4.40.1
+- Pytorch 2.2.1+cu121
+- Datasets 2.19.0
+- Tokenizers 0.19.1

runs/May01_21-26-51_2dbbf50fa8a4/events.out.tfevents.1714598826.2dbbf50fa8a4.1499.2

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