End of training

README.md

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+---
+license: mit
+base_model: flax-community/indonesian-roberta-base
+tags:
+- generated_from_trainer
+datasets:
+- indonlu
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: indonesian-roberta-base-nerp-tagging
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: indonlu
+      type: indonlu
+      config: nerp
+      split: validation
+      args: nerp
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.8102477477477478
+    - name: Recall
+      type: recall
+      value: 0.8107042253521127
+    - name: F1
+      type: f1
+      value: 0.8104759222754154
+    - name: Accuracy
+      type: accuracy
+      value: 0.9615076182838813
+---
+
+<!-- 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. -->
+
+# indonesian-roberta-base-nerp-tagging
+
+This model is a fine-tuned version of [flax-community/indonesian-roberta-base](https://huggingface.co/flax-community/indonesian-roberta-base) on the indonlu dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1180
+- Precision: 0.8102
+- Recall: 0.8107
+- F1: 0.8105
+- Accuracy: 0.9615
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 420  | 0.1419          | 0.7491    | 0.8034 | 0.7753 | 0.9551   |
+| 0.2261        | 2.0   | 840  | 0.1317          | 0.7889    | 0.7983 | 0.7936 | 0.9569   |
+| 0.1081        | 3.0   | 1260 | 0.1430          | 0.7587    | 0.8300 | 0.7927 | 0.9546   |
+| 0.0777        | 4.0   | 1680 | 0.1459          | 0.7848    | 0.8266 | 0.8052 | 0.9577   |
+| 0.0563        | 5.0   | 2100 | 0.1525          | 0.7923    | 0.8125 | 0.8022 | 0.9579   |
+| 0.0441        | 6.0   | 2520 | 0.1552          | 0.7986    | 0.8176 | 0.8080 | 0.9584   |
+| 0.0441        | 7.0   | 2940 | 0.1692          | 0.7910    | 0.8232 | 0.8068 | 0.9584   |
+| 0.0387        | 8.0   | 3360 | 0.1677          | 0.7894    | 0.8306 | 0.8095 | 0.9588   |
+| 0.032         | 9.0   | 3780 | 0.1784          | 0.7939    | 0.8249 | 0.8091 | 0.9586   |
+| 0.0284        | 10.0  | 4200 | 0.1817          | 0.7950    | 0.8261 | 0.8102 | 0.9588   |
+
+
+### Framework versions
+
+- Transformers 4.37.2
+- Pytorch 2.2.0+cu118
+- Datasets 2.16.1
+- Tokenizers 0.15.1

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