Training completed!

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,72 @@
+---
+license: mit
+base_model: xlm-roberta-base
+tags:
+- generated_from_trainer
+metrics:
+- f1
+- accuracy
+model-index:
+- name: xlm_roberta_kriter
+  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. -->
+
+# xlm_roberta_kriter
+
+This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1944
+- F1: 0.7931
+- Roc Auc: 0.8977
+- Accuracy: 0.7656
+
+## 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 |
+|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|:--------:|
+| 0.3583        | 1.0   | 1151  | 0.3227          | 0.1209 | 0.5303  | 0.4766   |
+| 0.289         | 2.0   | 2302  | 0.2266          | 0.6301 | 0.7660  | 0.6719   |
+| 0.214         | 3.0   | 3453  | 0.1938          | 0.7500 | 0.8319  | 0.7344   |
+| 0.1901        | 4.0   | 4604  | 0.1990          | 0.7328 | 0.8522  | 0.7188   |
+| 0.1646        | 5.0   | 5755  | 0.1865          | 0.7664 | 0.8626  | 0.7344   |
+| 0.1507        | 6.0   | 6906  | 0.1760          | 0.8030 | 0.8955  | 0.7773   |
+| 0.1247        | 7.0   | 8057  | 0.1797          | 0.8010 | 0.9033  | 0.7695   |
+| 0.1084        | 8.0   | 9208  | 0.1869          | 0.8051 | 0.8918  | 0.7812   |
+| 0.0767        | 9.0   | 10359 | 0.1942          | 0.7931 | 0.8977  | 0.7617   |
+| 0.0796        | 10.0  | 11510 | 0.1944          | 0.7931 | 0.8977  | 0.7656   |
+
+
+### Framework versions
+
+- Transformers 4.41.1
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1

config.json

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+{
+  "_name_or_path": "xlm-roberta-base",
+  "architectures": [
+    "XLMRobertaForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "i\u015flemci",
+    "1": "ekran_karti",
+    "2": "batarya",
+    "3": "ram",
+    "4": "ssd",
+    "5": "fiyat"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "batarya": 2,
+    "ekran_karti": 1,
+    "fiyat": 5,
+    "i\u015flemci": 0,
+    "ram": 3,
+    "ssd": 4
+  },
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "problem_type": "multi_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

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special_tokens_map.json

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tokenizer_config.json

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+}

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