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End of training

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README.md ADDED
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+ ---
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+ license: mit
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+ base_model: indobenchmark/indobert-large-p2
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: pertama
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # pertama
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+
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+ This model is a fine-tuned version of [indobenchmark/indobert-large-p2](https://huggingface.co/indobenchmark/indobert-large-p2) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 2.4507
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+ - F1 macro: 0.4131
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+ - Weighted: 0.5840
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+ - Balanced accuracy: 0.5423
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 1e-05
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 14
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | F1 macro | Weighted | Balanced accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------------:|
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+ | 1.3416 | 1.0 | 154 | 1.5603 | 0.2942 | 0.3462 | 0.4357 |
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+ | 0.941 | 2.0 | 308 | 1.3408 | 0.3530 | 0.5202 | 0.4807 |
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+ | 0.6965 | 3.0 | 462 | 1.3731 | 0.3747 | 0.5629 | 0.5101 |
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+ | 0.4375 | 4.0 | 616 | 1.3137 | 0.3904 | 0.5961 | 0.5002 |
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+ | 0.2491 | 5.0 | 770 | 1.5577 | 0.3772 | 0.5930 | 0.4978 |
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+ | 0.0793 | 6.0 | 924 | 2.1326 | 0.3923 | 0.5382 | 0.5401 |
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+ | 0.0488 | 7.0 | 1078 | 2.2000 | 0.3861 | 0.5483 | 0.5243 |
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+ | 0.0206 | 8.0 | 1232 | 2.1568 | 0.3914 | 0.5873 | 0.5096 |
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+ | 0.0243 | 9.0 | 1386 | 2.2272 | 0.4118 | 0.5851 | 0.5457 |
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+ | 0.0126 | 10.0 | 1540 | 2.3494 | 0.4029 | 0.5885 | 0.5346 |
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+ | 0.0449 | 11.0 | 1694 | 2.2914 | 0.4115 | 0.6037 | 0.5387 |
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+ | 0.0023 | 12.0 | 1848 | 2.5714 | 0.3962 | 0.5675 | 0.5334 |
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+ | 0.0023 | 13.0 | 2002 | 2.4491 | 0.4155 | 0.5878 | 0.5400 |
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+ | 0.0024 | 14.0 | 2156 | 2.4507 | 0.4131 | 0.5840 | 0.5423 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.41.2
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+ - Pytorch 2.3.0+cu121
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+ - Datasets 2.20.0
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+ - Tokenizers 0.19.1
config.json ADDED
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+ {
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+ "_name_or_path": "indobenchmark/indobert-large-p2",
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+ "_num_labels": 5,
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+ "architectures": [
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+ "BertForSequenceClassification"
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+ ],
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+ "attention_probs_dropout_prob": 0.1,
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+ "classifier_dropout": null,
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+ "directionality": "bidi",
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.1,
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+ "hidden_size": 1024,
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+ "id2label": {
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+ "0": "Sumber Daya Alam",
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+ "1": "Politik",
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+ "2": "Demografi",
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+ "3": "Pertahanan dan Keamanan",
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+ "4": "Ideologi",
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+ "5": "Ekonomi",
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+ "6": "Sosial Budaya",
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+ "7": "Geografi"
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+ },
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+ "initializer_range": 0.02,
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+ "intermediate_size": 4096,
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+ "label2id": {
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+ "LABEL_0": 0,
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+ "LABEL_1": 1,
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+ "LABEL_2": 2,
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+ "LABEL_3": 3,
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+ "LABEL_4": 4
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+ },
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+ "layer_norm_eps": 1e-12,
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+ "max_position_embeddings": 512,
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+ "model_type": "bert",
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+ "num_attention_heads": 16,
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+ "num_hidden_layers": 24,
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+ "output_past": true,
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+ "pad_token_id": 0,
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+ "pooler_fc_size": 768,
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+ "pooler_num_attention_heads": 12,
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+ "pooler_num_fc_layers": 3,
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+ "pooler_size_per_head": 128,
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+ "pooler_type": "first_token_transform",
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+ "position_embedding_type": "absolute",
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+ "problem_type": "single_label_classification",
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.41.2",
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+ "type_vocab_size": 2,
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+ "use_cache": true,
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+ "vocab_size": 30522
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+ }
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