Training fold 3

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: best_berita_bert_model_fold_3
+  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_berita_bert_model_fold_3
+
+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: 0.1732
+- Accuracy: 0.9808
+- Precision: 0.9809
+- Recall: 0.9811
+- F1: 0.9809
+
+## 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     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 0.5469        | 1.0   | 601  | 0.2814          | 0.9409   | 0.9416    | 0.9414 | 0.9410 |
+| 0.21          | 2.0   | 1202 | 0.1697          | 0.9600   | 0.9602    | 0.9605 | 0.9600 |
+| 0.1002        | 3.0   | 1803 | 0.2227          | 0.9667   | 0.9674    | 0.9673 | 0.9666 |
+| 0.0847        | 4.0   | 2404 | 0.2771          | 0.9584   | 0.9599    | 0.9592 | 0.9581 |
+| 0.029         | 5.0   | 3005 | 0.1732          | 0.9808   | 0.9809    | 0.9811 | 0.9809 |
+| 0.0095        | 6.0   | 3606 | 0.2415          | 0.9734   | 0.9737    | 0.9738 | 0.9733 |
+| 0.0134        | 7.0   | 4207 | 0.2048          | 0.9767   | 0.9769    | 0.9771 | 0.9766 |
+| 0.0001        | 8.0   | 4808 | 0.2916          | 0.9692   | 0.9697    | 0.9698 | 0.9691 |
+| 0.0039        | 9.0   | 5409 | 0.2201          | 0.9784   | 0.9786    | 0.9787 | 0.9784 |
+| 0.0           | 10.0  | 6010 | 0.2293          | 0.9742   | 0.9745    | 0.9746 | 0.9742 |
+
+
+### 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/bert-base-indonesian-1.5G-sentiment-analysis-smsa",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "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-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 32000
+}

model.safetensors

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runs/Jul17_11-24-47_1f7310198043/events.out.tfevents.1721215487.1f7310198043.34.0

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special_tokens_map.json

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tokenizer_config.json

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+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

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