Training fold 4

README.md

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+---
+license: mit
+base_model: ayameRushia/roberta-base-indonesian-sentiment-analysis-smsa
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: best_roberta_model_fold_4
+  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. -->
+
+# best_roberta_model_fold_4
+
+This model is a fine-tuned version of [ayameRushia/roberta-base-indonesian-sentiment-analysis-smsa](https://huggingface.co/ayameRushia/roberta-base-indonesian-sentiment-analysis-smsa) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.2676
+- Accuracy: 0.8546
+- Precision: 0.8334
+- Recall: 0.8336
+- F1: 0.8334
+
+## 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: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| No log        | 1.0   | 252  | 0.4839          | 0.8446   | 0.8238    | 0.8057 | 0.8132 |
+| 0.4291        | 2.0   | 504  | 0.8905          | 0.8287   | 0.8060    | 0.8194 | 0.8089 |
+| 0.4291        | 3.0   | 756  | 0.9640          | 0.8426   | 0.8142    | 0.8170 | 0.8156 |
+| 0.1372        | 4.0   | 1008 | 1.0182          | 0.8406   | 0.8198    | 0.8103 | 0.8139 |
+| 0.1372        | 5.0   | 1260 | 1.1472          | 0.8386   | 0.8238    | 0.8051 | 0.8134 |
+| 0.0264        | 6.0   | 1512 | 1.2455          | 0.8367   | 0.8145    | 0.8022 | 0.8078 |
+| 0.0264        | 7.0   | 1764 | 1.3009          | 0.8406   | 0.8221    | 0.8074 | 0.8139 |
+| 0.0072        | 8.0   | 2016 | 1.3084          | 0.8486   | 0.8314    | 0.8187 | 0.8238 |
+| 0.0072        | 9.0   | 2268 | 1.2815          | 0.8486   | 0.8272    | 0.8258 | 0.8265 |
+| 0.0063        | 10.0  | 2520 | 1.2676          | 0.8546   | 0.8334    | 0.8336 | 0.8334 |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.1.2
+- Datasets 2.19.2
+- Tokenizers 0.19.1

config.json

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+{
+  "_name_or_path": "ayameRushia/roberta-base-indonesian-sentiment-analysis-smsa",
+  "architectures": [
+    "RobertaForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "POSITIVE",
+    "1": "NEUTRAL",
+    "2": "NEGATIVE"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "NEGATIVE": 2,
+    "NEUTRAL": 1,
+    "POSITIVE": 0
+  },
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 50265
+}

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training_args.bin

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