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README.md

@@ -1,3 +1,75 @@
 ---
 license: apache-2.0
+language:
+- tr
+metrics:
+- accuracy
+library_name: transformers
+pipeline_tag: text-classification
+widget:
+  - text: >-
+      HATAY DEFNE İLÇESİNE yardımlar gitmiyor Özellikle çadıra battaniyeye yiyeceğe ihtiyaç var. Antakyanın dışında olduğu için tüm yardimlar 
+      İSKENDERUNA ANTAKYAYA gidiyor. Bu bölgeye gitmiyor..DEFNE İLÇESİNE GİDECEK ERZAK Çadır yardımlarını
+    example_title: Örnek
 ---
+**Train-Test Set:** "intent-multilabel-v1-2.zip"
+
+**Model:** "dbmdz/bert-base-turkish-cased"
+
+## Tokenizer Params
+```
+max_length=128
+padding="max_length"
+truncation=True
+```
+
+## Training Params
+```
+evaluation_strategy = "epoch"
+save_strategy = "epoch"
+per_device_train_batch_size = 16
+per_device_eval_batch_size = 16
+num_train_epochs = 4
+load_best_model_at_end = True
+```
+
+## Train-Val Splitting Configuration
+```
+train_test_split(df_train,
+                 test_size=0.1,
+                 random_state=1111)
+```
+
+## Training Log
+```
+Epoch	Training Loss	Validation Loss
+1	    No log	        0.150276
+2	    0.195100	    0.132906
+3	    0.107700	    0.128633
+4	    0.107700	    0.127795
+```
+
+## Threshold Optimization
+- **Best Threshold:** 0.1
+- **F1 @ Threshold:** 0.734
+
+## Eval Results
+```
+              precision    recall  f1-score   support
+
+    Alakasiz       0.90      0.87      0.89       734
+     Barinma       0.85      0.80      0.83       207
+  Elektronik       0.73      0.78      0.75       130
+       Giysi       0.83      0.66      0.73        94
+    Kurtarma       0.86      0.79      0.82       362
+    Lojistik       0.73      0.51      0.60       112
+      Saglik       0.74      0.74      0.74       108
+          Su       0.64      0.60      0.62        78
+       Yagma       0.68      0.55      0.61        31
+       Yemek       0.80      0.83      0.81       117
+
+   micro avg       0.84      0.79      0.81      1973
+   macro avg       0.78      0.71      0.74      1973
+weighted avg       0.84      0.79      0.81      1973
+ samples avg       0.84      0.82      0.82      1973
+```

config.json

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+{
+  "_name_or_path": "dbmdz/bert-base-turkish-cased",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "Alakasiz",
+    "1": "Barinma",
+    "2": "Elektronik",
+    "3": "Giysi",
+    "4": "Kurtarma",
+    "5": "Lojistik",
+    "6": "Saglik",
+    "7": "Su",
+    "8": "Yagma",
+    "9": "Yemek"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "Alakasiz": 0,
+    "Barinma": 1,
+    "Elektronik": 2,
+    "Giysi": 3,
+    "Kurtarma": 4,
+    "Lojistik": 5,
+    "Saglik": 6,
+    "Su": 7,
+    "Yagma": 8,
+    "Yemek": 9
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "multi_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.26.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 32000
+}

optimizer.pt

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+size 885118085

pytorch_model.bin

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rng_state.pth

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scheduler.pt

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+size 627

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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+{
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
+  "mask_token": "[MASK]",
+  "max_len": 512,
+  "model_max_length": 512,
+  "name_or_path": "dbmdz/bert-base-turkish-cased",
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "special_tokens_map_file": null,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

trainer_state.json

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+{
+  "best_metric": 0.12779545783996582,
+  "best_model_checkpoint": "turkish_multilabel_intent_bert-base-turkish-cased/checkpoint-1392",
+  "epoch": 4.0,
+  "global_step": 1392,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
+    {
+      "epoch": 1.0,
+      "eval_loss": 0.15027593076229095,
+      "eval_runtime": 0.4945,
+      "eval_samples_per_second": 1249.718,
+      "eval_steps_per_second": 78.866,
+      "step": 348
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+    {
+      "epoch": 1.44,
+      "learning_rate": 3.2040229885057474e-05,
+      "loss": 0.1951,
+      "step": 500
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+    {
+      "epoch": 2.0,
+      "eval_loss": 0.1329057365655899,
+      "eval_runtime": 0.4918,
+      "eval_samples_per_second": 1256.527,
+      "eval_steps_per_second": 79.295,
+      "step": 696
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+    {
+      "epoch": 2.87,
+      "learning_rate": 1.4080459770114942e-05,
+      "loss": 0.1077,
+      "step": 1000
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+    {
+      "epoch": 3.0,
+      "eval_loss": 0.12863321602344513,
+      "eval_runtime": 0.4914,
+      "eval_samples_per_second": 1257.609,
+      "eval_steps_per_second": 79.364,
+      "step": 1044
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+    {
+      "epoch": 4.0,
+      "eval_loss": 0.12779545783996582,
+      "eval_runtime": 0.4919,
+      "eval_samples_per_second": 1256.296,
+      "eval_steps_per_second": 79.281,
+      "step": 1392
+    }
+  ],
+  "max_steps": 1392,
+  "num_train_epochs": 4,
+  "total_flos": 1461686896158720.0,
+  "trial_name": null,
+  "trial_params": null
+}

training_args.bin

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