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_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_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: 1.2908
+- Accuracy: 0.8386
+- Precision: 0.8281
+- Recall: 0.7986
+- F1: 0.8101
+
+## 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  | 0.6290          | 0.8008   | 0.7903    | 0.7322 | 0.7457 |
+| 0.5166        | 2.0   | 504  | 0.6945          | 0.8068   | 0.8131    | 0.7396 | 0.7568 |
+| 0.5166        | 3.0   | 756  | 0.9795          | 0.8108   | 0.7953    | 0.7652 | 0.7721 |
+| 0.1546        | 4.0   | 1008 | 1.1504          | 0.8187   | 0.8024    | 0.7829 | 0.7902 |
+| 0.1546        | 5.0   | 1260 | 1.2908          | 0.8386   | 0.8281    | 0.7986 | 0.8101 |
+| 0.0243        | 6.0   | 1512 | 1.2868          | 0.8247   | 0.8043    | 0.7947 | 0.7988 |
+| 0.0243        | 7.0   | 1764 | 1.4339          | 0.8307   | 0.8214    | 0.7823 | 0.7949 |
+| 0.0077        | 8.0   | 2016 | 1.4287          | 0.8327   | 0.8222    | 0.7845 | 0.7978 |
+| 0.0077        | 9.0   | 2268 | 1.4630          | 0.8287   | 0.8098    | 0.7842 | 0.7941 |
+| 0.0001        | 10.0  | 2520 | 1.4618          | 0.8307   | 0.8129    | 0.7863 | 0.7966 |
+
+
+### 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/Jul10_06-54-34_8d2ffc259edc/events.out.tfevents.1720594475.8d2ffc259edc.34.0

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runs/Jul10_06-54-34_8d2ffc259edc/events.out.tfevents.1720595730.8d2ffc259edc.34.1

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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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