Training completed!

README.md

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+---
+license: mit
+base_model: dbmdz/distilbert-base-turkish-cased
+tags:
+- generated_from_trainer
+metrics:
+- f1
+- accuracy
+model-index:
+- name: distilbert_turk
+  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. -->
+
+# distilbert_turk
+
+This model is a fine-tuned version of [dbmdz/distilbert-base-turkish-cased](https://huggingface.co/dbmdz/distilbert-base-turkish-cased) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1927
+- F1: 0.8338
+- Roc Auc: 0.9092
+- Accuracy: 0.8047
+
+## 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: 2
+- eval_batch_size: 2
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | F1     | Roc Auc | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|:--------:|
+| 0.2899        | 1.0   | 1151  | 0.2053          | 0.6418 | 0.7738  | 0.6719   |
+| 0.1846        | 2.0   | 2302  | 0.1777          | 0.7480 | 0.8434  | 0.7461   |
+| 0.1432        | 3.0   | 3453  | 0.1633          | 0.7879 | 0.8866  | 0.7656   |
+| 0.1241        | 4.0   | 4604  | 0.1508          | 0.8256 | 0.9037  | 0.7891   |
+| 0.0961        | 5.0   | 5755  | 0.1621          | 0.8203 | 0.9048  | 0.7969   |
+| 0.065         | 6.0   | 6906  | 0.1733          | 0.8108 | 0.9092  | 0.7969   |
+| 0.0548        | 7.0   | 8057  | 0.1848          | 0.8238 | 0.8993  | 0.7930   |
+| 0.0496        | 8.0   | 9208  | 0.1875          | 0.8130 | 0.9055  | 0.7969   |
+| 0.0413        | 9.0   | 10359 | 0.1905          | 0.8359 | 0.9096  | 0.8086   |
+| 0.038         | 10.0  | 11510 | 0.1927          | 0.8338 | 0.9092  | 0.8047   |
+
+
+### Framework versions
+
+- Transformers 4.37.0
+- Pytorch 2.1.0+cu121
+- Datasets 2.16.1
+- Tokenizers 0.15.0

config.json

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+{
+  "_name_or_path": "dbmdz/distilbert-base-turkish-cased",
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForSequenceClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "id2label": {
+    "0": "i\u015flemci",
+    "1": "ekran_karti",
+    "2": "batarya",
+    "3": "ram",
+    "4": "ssd",
+    "5": "fiyat"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "batarya": 2,
+    "ekran_karti": 1,
+    "fiyat": 5,
+    "i\u015flemci": 0,
+    "ram": 3,
+    "ssd": 4
+  },
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "output_past": true,
+  "pad_token_id": 0,
+  "problem_type": "multi_label_classification",
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": true,
+  "tie_weights_": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.37.0",
+  "vocab_size": 32000
+}

model.safetensors

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runs/Jan24_12-04-43_b313c7a6aaee/events.out.tfevents.1706097884.b313c7a6aaee.198.0

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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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+      "lstrip": false,
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+      "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]",
+  "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": "DistilBertTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

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