update model card README.md

README.md

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+---
+tags:
+- generated_from_trainer
+datasets:
+- nerde
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: NERDE-base
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: nerde
+      type: nerde
+      args: NERDE
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.9118601747815231
+    - name: Recall
+      type: recall
+      value: 0.9152882205513785
+    - name: F1
+      type: f1
+      value: 0.9135709818636648
+    - name: Accuracy
+      type: accuracy
+      value: 0.9841962132484992
+---
+
+<!-- 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. -->
+
+# NERDE-base
+
+This model is a fine-tuned version of [pierreguillou/bert-base-cased-pt-lenerbr](https://huggingface.co/pierreguillou/bert-base-cased-pt-lenerbr) on the nerde dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1246
+- Precision: 0.9119
+- Recall: 0.9153
+- F1: 0.9136
+- Accuracy: 0.9842
+
+## 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: 2e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.2466        | 1.0   | 541  | 0.1003          | 0.8515    | 0.8822 | 0.8666 | 0.9782   |
+| 0.0608        | 2.0   | 1082 | 0.0855          | 0.8990    | 0.9083 | 0.9036 | 0.9837   |
+| 0.0411        | 3.0   | 1623 | 0.1006          | 0.9078    | 0.9103 | 0.9090 | 0.9837   |
+| 0.0266        | 4.0   | 2164 | 0.1052          | 0.9023    | 0.9163 | 0.9092 | 0.9828   |
+| 0.0191        | 5.0   | 2705 | 0.1060          | 0.9112    | 0.9183 | 0.9147 | 0.9847   |
+| 0.0153        | 6.0   | 3246 | 0.1152          | 0.9052    | 0.9098 | 0.9075 | 0.9831   |
+| 0.0124        | 7.0   | 3787 | 0.1209          | 0.9029    | 0.9185 | 0.9107 | 0.9835   |
+| 0.0083        | 8.0   | 4328 | 0.1176          | 0.9072    | 0.9163 | 0.9117 | 0.9844   |
+| 0.0077        | 9.0   | 4869 | 0.1240          | 0.9080    | 0.9201 | 0.9140 | 0.9844   |
+| 0.0051        | 10.0  | 5410 | 0.1246          | 0.9119    | 0.9153 | 0.9136 | 0.9842   |
+
+
+### Framework versions
+
+- Transformers 4.20.1
+- Pytorch 1.12.0+cu113
+- Datasets 2.3.2
+- Tokenizers 0.12.1