Model save

README.md

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+---
+license: mit
+base_model: ayameRushia/bert-base-indonesian-1.5G-sentiment-analysis-smsa
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: stunting-medsos-sentiment
+  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. -->
+
+# stunting-medsos-sentiment
+
+This model is a fine-tuned version of [ayameRushia/bert-base-indonesian-1.5G-sentiment-analysis-smsa](https://huggingface.co/ayameRushia/bert-base-indonesian-1.5G-sentiment-analysis-smsa) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.9685
+- Accuracy: 0.8360
+- Precision: 0.7971
+- Recall: 0.7985
+- F1: 0.7976
+
+## 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
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| No log        | 1.0   | 314  | 0.5221          | 0.8105   | 0.7868    | 0.7345 | 0.7517 |
+| 0.5607        | 2.0   | 628  | 0.6322          | 0.8296   | 0.7991    | 0.7670 | 0.7794 |
+| 0.5607        | 3.0   | 942  | 0.9147          | 0.8201   | 0.7929    | 0.7655 | 0.7731 |
+| 0.2289        | 4.0   | 1256 | 0.9736          | 0.8296   | 0.7929    | 0.7990 | 0.7957 |
+| 0.0597        | 5.0   | 1570 | 0.9685          | 0.8360   | 0.7971    | 0.7985 | 0.7976 |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.1.2
+- Datasets 2.19.2
+- Tokenizers 0.19.1

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