End of training

README.md

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+---
+license: mit
+base_model: indobenchmark/indobert-large-p2
+tags:
+- generated_from_trainer
+model-index:
+- name: classification-hate-speech-DE-7epoch-idblp2
+  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. -->
+
+# classification-hate-speech-DE-7epoch-idblp2
+
+This model is a fine-tuned version of [indobenchmark/indobert-large-p2](https://huggingface.co/indobenchmark/indobert-large-p2) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 2.1842
+- F1 macro: 0.3914
+- Weighted: 0.5669
+- Balanced accuracy: 0.5343
+
+## 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: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 7
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | F1 macro | Weighted | Balanced accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------------:|
+| 1.4198        | 1.0   | 38   | 1.4182          | 0.3423   | 0.4674   | 0.4787            |
+| 0.9738        | 2.0   | 76   | 1.1739          | 0.4094   | 0.6456   | 0.5125            |
+| 0.4162        | 3.0   | 114  | 1.5137          | 0.4162   | 0.6046   | 0.5367            |
+| 0.1499        | 4.0   | 152  | 1.9714          | 0.3981   | 0.5371   | 0.5527            |
+| 0.0389        | 5.0   | 190  | 2.1858          | 0.3819   | 0.5532   | 0.5416            |
+| 0.0144        | 6.0   | 228  | 2.1318          | 0.3890   | 0.5780   | 0.5369            |
+| 0.0195        | 7.0   | 266  | 2.1842          | 0.3914   | 0.5669   | 0.5343            |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1

config.json

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+{
+  "_name_or_path": "indobenchmark/indobert-large-p2",
+  "_num_labels": 5,
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3",
+    "4": "LABEL_4",
+    "5": "LABEL_5",
+    "6": "LABEL_6",
+    "7": "LABEL_7"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2,
+    "LABEL_3": 3,
+    "LABEL_4": 4,
+    "LABEL_5": 5,
+    "LABEL_6": 6,
+    "LABEL_7": 7
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "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": 30522
+}

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runs/Jun19_15-40-16_6e71a2621ca0/events.out.tfevents.1718811618.6e71a2621ca0.1065.8

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training_args.bin

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