End of training

README.md

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+---
+base_model: monologg/koelectra-small-v3-discriminator
+tags:
+- generated_from_trainer
+model-index:
+- name: find_tune_bert_output
+  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. -->
+
+# find_tune_bert_output
+
+This model is a fine-tuned version of [monologg/koelectra-small-v3-discriminator](https://huggingface.co/monologg/koelectra-small-v3-discriminator) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2110
+- Overall Precision: 0.8468
+- Overall Recall: 0.8561
+- Overall F1: 0.8514
+- Overall Accuracy: 0.9405
+- Loc F1: 0.9090
+- Org F1: 0.7685
+- Per F1: 0.8477
+
+## 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: 2e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- 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 | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Loc F1 | Org F1 | Per F1 |
+|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:------:|:------:|:------:|
+| 0.2146        | 0.8   | 1000 | 0.2903          | 0.7632            | 0.8340         | 0.7970     | 0.9175           | 0.8729 | 0.6812 | 0.7966 |
+| 0.2538        | 1.6   | 2000 | 0.2374          | 0.8183            | 0.8290         | 0.8236     | 0.9299           | 0.8940 | 0.7187 | 0.8178 |
+| 0.2192        | 2.4   | 3000 | 0.2265          | 0.8246            | 0.8437         | 0.8340     | 0.9340           | 0.8956 | 0.7403 | 0.8322 |
+| 0.1967        | 3.2   | 4000 | 0.2206          | 0.8261            | 0.8529         | 0.8393     | 0.9354           | 0.9047 | 0.7499 | 0.8290 |
+| 0.1814        | 4.0   | 5000 | 0.2169          | 0.8371            | 0.8538         | 0.8453     | 0.9379           | 0.9057 | 0.7605 | 0.8388 |
+| 0.1661        | 4.8   | 6000 | 0.2169          | 0.8403            | 0.8490         | 0.8446     | 0.9382           | 0.9050 | 0.7583 | 0.8378 |
+| 0.1577        | 5.6   | 7000 | 0.2116          | 0.8413            | 0.8604         | 0.8507     | 0.9401           | 0.9088 | 0.7670 | 0.8472 |
+| 0.1544        | 6.4   | 8000 | 0.2110          | 0.8468            | 0.8561         | 0.8514     | 0.9405           | 0.9090 | 0.7685 | 0.8477 |
+
+
+### Framework versions
+
+- Transformers 4.38.2
+- Pytorch 2.2.1+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.2

config.json

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+{
+  "_name_or_path": "monologg/koelectra-small-v3-discriminator",
+  "architectures": [
+    "ElectraForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "embedding_size": 128,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 256,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3",
+    "4": "LABEL_4",
+    "5": "LABEL_5",
+    "6": "LABEL_6"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 1024,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2,
+    "LABEL_3": 3,
+    "LABEL_4": 4,
+    "LABEL_5": 5,
+    "LABEL_6": 6
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "electra",
+  "num_attention_heads": 4,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "summary_activation": "gelu",
+  "summary_last_dropout": 0.1,
+  "summary_type": "first",
+  "summary_use_proj": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.38.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 35000
+}

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special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "special": true
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+      "special": true
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "ElectraTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

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