End of training

README.md

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+---
+license: mit
+base_model: indolem/indobert-base-uncased
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: Zidan_model_output_v8
+  results: []
+---
+
+<!-- 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. -->
+
+# Zidan_model_output_v8
+
+This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.5008
+- Accuracy: 0.7818
+
+## 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: 4
+- eval_batch_size: 4
+- 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 | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| No log        | 1.0   | 248  | 0.9353          | 0.4545   |
+| No log        | 2.0   | 496  | 0.8453          | 0.7636   |
+| 0.8008        | 3.0   | 744  | 0.9547          | 0.8364   |
+| 0.8008        | 4.0   | 992  | 0.9477          | 0.8      |
+| 0.3923        | 5.0   | 1240 | 1.2342          | 0.8      |
+| 0.3923        | 6.0   | 1488 | 1.5085          | 0.8      |
+| 0.1679        | 7.0   | 1736 | 1.2791          | 0.8182   |
+| 0.1679        | 8.0   | 1984 | 1.3501          | 0.8182   |
+| 0.0693        | 9.0   | 2232 | 1.5200          | 0.7818   |
+| 0.0693        | 10.0  | 2480 | 1.5008          | 0.7818   |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1

model.safetensors

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