be2be2/koelectra_emotion_v2

README.md

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+---
+library_name: transformers
+base_model: monologg/koelectra-small-finetuned-sentiment
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+model-index:
+- name: koelectra_emotion_v2
+  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. -->
+
+# koelectra_emotion_v2
+
+This model is a fine-tuned version of [monologg/koelectra-small-finetuned-sentiment](https://huggingface.co/monologg/koelectra-small-finetuned-sentiment) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.9556
+- Accuracy: 0.6876
+- F1: 0.6854
+
+## 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: 64
+- eval_batch_size: 64
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
+| 1.2179        | 1.0   | 1466  | 0.9643          | 0.6553   | 0.6470 |
+| 0.9217        | 2.0   | 2932  | 0.8961          | 0.6749   | 0.6701 |
+| 0.8429        | 3.0   | 4398  | 0.8783          | 0.6882   | 0.6808 |
+| 0.7853        | 4.0   | 5864  | 0.8862          | 0.6876   | 0.6835 |
+| 0.7381        | 5.0   | 7330  | 0.8941          | 0.6867   | 0.6812 |
+| 0.6967        | 6.0   | 8796  | 0.9089          | 0.6884   | 0.6821 |
+| 0.6581        | 7.0   | 10262 | 0.9189          | 0.6869   | 0.6839 |
+| 0.6306        | 8.0   | 11728 | 0.9284          | 0.6876   | 0.6853 |
+| 0.6059        | 9.0   | 13194 | 0.9519          | 0.6876   | 0.6840 |
+| 0.5879        | 10.0  | 14660 | 0.9556          | 0.6876   | 0.6854 |
+
+
+### Framework versions
+
+- Transformers 4.56.1
+- Pytorch 2.8.0+cu126
+- Datasets 4.0.0
+- Tokenizers 0.22.0

config.json

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+{
+  "_num_labels": 2,
+  "architectures": [
+    "ElectraForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "embedding_size": 128,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 256,
+  "id2label": {
+    "0": "\ubd88\uc548",
+    "1": "\ubd84\ub178",
+    "2": "\uc2ac\ud514",
+    "3": "\uc911\ub9bd",
+    "4": "\ud589\ubcf5",
+    "5": "\ud610\uc624",
+    "6": "\ub2f9\ud669"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 1024,
+  "label2id": {
+    "\ub2f9\ud669": 6,
+    "\ubd84\ub178": 1,
+    "\ubd88\uc548": 0,
+    "\uc2ac\ud514": 2,
+    "\uc911\ub9bd": 3,
+    "\ud589\ubcf5": 4,
+    "\ud610\uc624": 5
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "electra",
+  "num_attention_heads": 4,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "summary_activation": "gelu",
+  "summary_last_dropout": 0.1,
+  "summary_type": "first",
+  "summary_use_proj": true,
+  "transformers_version": "4.56.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 32200
+}

model.safetensors

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+oid sha256:7e5164b41b8f2af89b0f2ee88773282cb56e6ca122b33e4b057236b27414a70f
+size 55085476

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
+    },
+    "1": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+    "3": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+    "4": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "max_len": 512,
+  "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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