update model card README.md

README.md

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+---
+tags:
+- generated_from_trainer
+datasets:
+- funsd-layoutlmv3
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: lilt-roberta-en-base-finetuned-funsd
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: funsd-layoutlmv3
+      type: funsd-layoutlmv3
+      config: funsd
+      split: train
+      args: funsd
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.8761670761670761
+    - name: Recall
+      type: recall
+      value: 0.8857426726279185
+    - name: F1
+      type: f1
+      value: 0.8809288537549407
+    - name: Accuracy
+      type: accuracy
+      value: 0.8068465470105789
+---
+
+<!-- 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. -->
+
+# lilt-roberta-en-base-finetuned-funsd
+
+This model is a fine-tuned version of [nielsr/lilt-roberta-en-base](https://huggingface.co/nielsr/lilt-roberta-en-base) on the funsd-layoutlmv3 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.6552
+- Precision: 0.8762
+- Recall: 0.8857
+- F1: 0.8809
+- Accuracy: 0.8068
+
+## 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: 5e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 0.1
+- training_steps: 2000
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 5.26   | 100  | 1.1789          | 0.8506    | 0.8485 | 0.8495 | 0.7869   |
+| No log        | 10.53  | 200  | 1.2382          | 0.8360    | 0.8788 | 0.8569 | 0.7970   |
+| No log        | 15.79  | 300  | 1.3766          | 0.8557    | 0.8897 | 0.8724 | 0.7909   |
+| No log        | 21.05  | 400  | 1.5590          | 0.8368    | 0.8763 | 0.8561 | 0.7792   |
+| 0.04          | 26.32  | 500  | 1.4379          | 0.8562    | 0.8813 | 0.8685 | 0.7992   |
+| 0.04          | 31.58  | 600  | 1.5397          | 0.8593    | 0.8947 | 0.8766 | 0.8054   |
+| 0.04          | 36.84  | 700  | 1.6132          | 0.8621    | 0.8723 | 0.8672 | 0.7933   |
+| 0.04          | 42.11  | 800  | 1.6483          | 0.8566    | 0.8872 | 0.8716 | 0.7777   |
+| 0.04          | 47.37  | 900  | 1.6593          | 0.8641    | 0.8813 | 0.8726 | 0.7895   |
+| 0.0044        | 52.63  | 1000 | 1.6704          | 0.8595    | 0.8718 | 0.8656 | 0.7925   |
+| 0.0044        | 57.89  | 1100 | 1.6795          | 0.8495    | 0.8803 | 0.8646 | 0.7748   |
+| 0.0044        | 63.16  | 1200 | 1.5515          | 0.8604    | 0.8912 | 0.8755 | 0.7991   |
+| 0.0044        | 68.42  | 1300 | 1.6665          | 0.8573    | 0.8867 | 0.8718 | 0.7821   |
+| 0.0044        | 73.68  | 1400 | 1.5893          | 0.8604    | 0.8877 | 0.8738 | 0.7895   |
+| 0.0008        | 78.95  | 1500 | 1.5613          | 0.8603    | 0.8872 | 0.8736 | 0.8123   |
+| 0.0008        | 84.21  | 1600 | 1.5853          | 0.8521    | 0.8872 | 0.8693 | 0.8040   |
+| 0.0008        | 89.47  | 1700 | 1.6539          | 0.8707    | 0.8833 | 0.8769 | 0.8077   |
+| 0.0008        | 94.74  | 1800 | 1.6634          | 0.8787    | 0.8813 | 0.8800 | 0.8079   |
+| 0.0008        | 100.0  | 1900 | 1.6534          | 0.8810    | 0.8862 | 0.8836 | 0.8073   |
+| 0.0004        | 105.26 | 2000 | 1.6552          | 0.8762    | 0.8857 | 0.8809 | 0.8068   |
+
+
+### Framework versions
+
+- Transformers 4.23.0.dev0
+- Pytorch 1.12.1+cu113
+- Datasets 2.5.1
+- Tokenizers 0.13.0