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tone_berita_large-p2_v1

Text Classification
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bert
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Noctuaru/tone_berita_largep2_v1

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README.md ADDED
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+ ---
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+ library_name: transformers
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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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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: results
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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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+ # results
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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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+ - Accuracy: 0.8
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+ - F1 Weighted: 0.7852
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+ - Loss: 0.7316
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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: 2.7820079535067715e-05
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+ - train_batch_size: 64
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+ - eval_batch_size: 64
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+ - seed: 42
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+ - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - num_epochs: 17
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Accuracy | F1 Weighted | Validation Loss |
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+ |:-------------:|:-----:|:----:|:--------:|:-----------:|:---------------:|
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+ | No log | 1.0 | 6 | 0.24 | 0.1029 | 1.2189 |
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+ | 1.2118 | 2.0 | 12 | 0.32 | 0.2521 | 1.1035 |
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+ | 1.2118 | 3.0 | 18 | 0.64 | 0.5214 | 1.0049 |
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+ | 1.0516 | 4.0 | 24 | 0.68 | 0.6394 | 0.8500 |
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+ | 1.0014 | 5.0 | 30 | 0.64 | 0.628 | 0.8799 |
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+ | 1.0014 | 6.0 | 36 | 0.68 | 0.6835 | 0.7949 |
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+ | 1.1235 | 7.0 | 42 | 0.72 | 0.6931 | 0.8320 |
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+ | 1.1235 | 8.0 | 48 | 0.64 | 0.6368 | 0.7677 |
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+ | 1.0837 | 9.0 | 54 | 0.8 | 0.7852 | 0.7316 |
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+ | 0.9824 | 10.0 | 60 | 0.76 | 0.7324 | 0.7318 |
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+ | 0.9824 | 11.0 | 66 | 0.72 | 0.6966 | 0.7191 |
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+ | 0.8334 | 12.0 | 72 | 0.76 | 0.7346 | 0.7128 |
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+ | 0.8334 | 13.0 | 78 | 0.68 | 0.6430 | 0.7165 |
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+ | 0.7175 | 14.0 | 84 | 0.68 | 0.6430 | 0.7259 |
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+ | 0.6813 | 15.0 | 90 | 0.68 | 0.6430 | 0.7139 |
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+ | 0.6813 | 16.0 | 96 | 0.72 | 0.6981 | 0.6977 |
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+ | 0.6765 | 17.0 | 102 | 0.72 | 0.6981 | 0.6941 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.56.2
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+ - Pytorch 2.8.0+cu126
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+ - Datasets 4.0.0
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+ - Tokenizers 0.22.1
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+ {
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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": 0.3,
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+ "classifier_dropout": null,
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+ "directionality": "bidi",
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+ "dtype": "float32",
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.3,
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+ "hidden_size": 1024,
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+ "id2label": {
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+ "0": "LABEL_0",
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+ "1": "LABEL_1",
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+ "2": "LABEL_2"
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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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+ },
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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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+ "transformers_version": "4.56.2",
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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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