End of training

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.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 +1,121 @@
+---
+library_name: transformers
+license: mit
+base_model: FacebookAI/xlm-roberta-base
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: Roberta_combo_v1
+  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. -->
+
+# Roberta_combo_v1
+
+This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5245
+- Accuracy: 0.787
+- Auc: 0.888
+- Precision: 0.775
+- Recall: 0.868
+- F1: 0.819
+- F1-macro: 0.781
+- F1-micro: 0.787
+- F1-weighted: 0.785
+
+## 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: 8
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 32
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 5
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step  | Validation Loss | Accuracy | Auc   | Precision | Recall | F1    | F1-macro | F1-micro | F1-weighted |
+|:-------------:|:------:|:-----:|:---------------:|:--------:|:-----:|:---------:|:------:|:-----:|:--------:|:--------:|:-----------:|
+| 0.5526        | 0.1028 | 500   | 0.4682          | 0.749    | 0.847 | 0.732     | 0.863  | 0.792 | 0.737    | 0.749    | 0.744       |
+| 0.462         | 0.2057 | 1000  | 0.4818          | 0.759    | 0.857 | 0.738     | 0.879  | 0.802 | 0.747    | 0.759    | 0.753       |
+| 0.4466        | 0.3085 | 1500  | 0.4817          | 0.747    | 0.852 | 0.751     | 0.815  | 0.782 | 0.741    | 0.747    | 0.745       |
+| 0.43          | 0.4114 | 2000  | 0.4300          | 0.763    | 0.87  | 0.737     | 0.891  | 0.807 | 0.75     | 0.763    | 0.756       |
+| 0.4215        | 0.5142 | 2500  | 0.4412          | 0.763    | 0.869 | 0.725     | 0.924  | 0.812 | 0.746    | 0.763    | 0.753       |
+| 0.4148        | 0.6170 | 3000  | 0.4229          | 0.774    | 0.879 | 0.746     | 0.899  | 0.815 | 0.762    | 0.774    | 0.768       |
+| 0.4056        | 0.7199 | 3500  | 0.4351          | 0.772    | 0.88  | 0.747     | 0.89   | 0.813 | 0.761    | 0.772    | 0.767       |
+| 0.4094        | 0.8227 | 4000  | 0.4184          | 0.777    | 0.883 | 0.743     | 0.913  | 0.819 | 0.764    | 0.777    | 0.77        |
+| 0.403         | 0.9255 | 4500  | 0.4122          | 0.778    | 0.884 | 0.747     | 0.906  | 0.819 | 0.766    | 0.778    | 0.772       |
+| 0.3905        | 1.0284 | 5000  | 0.4450          | 0.776    | 0.884 | 0.738     | 0.924  | 0.821 | 0.761    | 0.776    | 0.768       |
+| 0.3826        | 1.1312 | 5500  | 0.4171          | 0.78     | 0.885 | 0.754     | 0.895  | 0.819 | 0.769    | 0.78     | 0.775       |
+| 0.3743        | 1.2341 | 6000  | 0.4093          | 0.775    | 0.883 | 0.741     | 0.915  | 0.819 | 0.762    | 0.775    | 0.768       |
+| 0.3716        | 1.3369 | 6500  | 0.4804          | 0.77     | 0.876 | 0.739     | 0.904  | 0.813 | 0.756    | 0.77     | 0.763       |
+| 0.3748        | 1.4397 | 7000  | 0.4300          | 0.779    | 0.883 | 0.752     | 0.898  | 0.818 | 0.768    | 0.779    | 0.774       |
+| 0.3741        | 1.5426 | 7500  | 0.4144          | 0.779    | 0.886 | 0.774     | 0.85   | 0.81  | 0.773    | 0.779    | 0.777       |
+| 0.3732        | 1.6454 | 8000  | 0.4029          | 0.782    | 0.887 | 0.76      | 0.888  | 0.819 | 0.773    | 0.782    | 0.778       |
+| 0.3622        | 1.7483 | 8500  | 0.4722          | 0.78     | 0.885 | 0.764     | 0.874  | 0.815 | 0.772    | 0.78     | 0.777       |
+| 0.3738        | 1.8511 | 9000  | 0.4206          | 0.78     | 0.886 | 0.757     | 0.89   | 0.818 | 0.77     | 0.78     | 0.775       |
+| 0.3669        | 1.9539 | 9500  | 0.4150          | 0.784    | 0.891 | 0.77      | 0.872  | 0.818 | 0.777    | 0.784    | 0.781       |
+| 0.3562        | 2.0568 | 10000 | 0.4142          | 0.786    | 0.89  | 0.761     | 0.897  | 0.823 | 0.776    | 0.786    | 0.782       |
+| 0.3333        | 2.1596 | 10500 | 0.4845          | 0.782    | 0.887 | 0.751     | 0.908  | 0.822 | 0.77     | 0.782    | 0.776       |
+| 0.3396        | 2.2624 | 11000 | 0.4334          | 0.783    | 0.888 | 0.758     | 0.896  | 0.821 | 0.773    | 0.783    | 0.779       |
+| 0.339         | 2.3653 | 11500 | 0.4221          | 0.786    | 0.889 | 0.776     | 0.863  | 0.818 | 0.78     | 0.786    | 0.784       |
+| 0.33          | 2.4681 | 12000 | 0.4157          | 0.785    | 0.889 | 0.792     | 0.83   | 0.811 | 0.781    | 0.785    | 0.784       |
+| 0.3416        | 2.5710 | 12500 | 0.4277          | 0.785    | 0.889 | 0.766     | 0.882  | 0.82  | 0.776    | 0.785    | 0.781       |
+| 0.3439        | 2.6738 | 13000 | 0.4299          | 0.784    | 0.888 | 0.784     | 0.844  | 0.813 | 0.779    | 0.784    | 0.783       |
+| 0.3468        | 2.7766 | 13500 | 0.4138          | 0.785    | 0.888 | 0.77      | 0.872  | 0.818 | 0.777    | 0.785    | 0.782       |
+| 0.3413        | 2.8795 | 14000 | 0.4512          | 0.786    | 0.89  | 0.784     | 0.85   | 0.815 | 0.781    | 0.786    | 0.785       |
+| 0.3334        | 2.9823 | 14500 | 0.4380          | 0.783    | 0.888 | 0.765     | 0.879  | 0.818 | 0.775    | 0.783    | 0.779       |
+| 0.3094        | 3.0852 | 15000 | 0.4798          | 0.785    | 0.889 | 0.77      | 0.873  | 0.818 | 0.777    | 0.785    | 0.782       |
+| 0.3156        | 3.1880 | 15500 | 0.4743          | 0.784    | 0.887 | 0.793     | 0.826  | 0.809 | 0.78     | 0.784    | 0.783       |
+| 0.3125        | 3.2908 | 16000 | 0.4991          | 0.784    | 0.888 | 0.764     | 0.884  | 0.819 | 0.775    | 0.784    | 0.78        |
+| 0.3097        | 3.3937 | 16500 | 0.4734          | 0.783    | 0.887 | 0.763     | 0.881  | 0.818 | 0.774    | 0.783    | 0.779       |
+| 0.3082        | 3.4965 | 17000 | 0.4715          | 0.784    | 0.887 | 0.783     | 0.846  | 0.813 | 0.779    | 0.784    | 0.783       |
+| 0.3024        | 3.5993 | 17500 | 0.4857          | 0.784    | 0.889 | 0.763     | 0.885  | 0.82  | 0.775    | 0.784    | 0.78        |
+| 0.3087        | 3.7022 | 18000 | 0.5177          | 0.781    | 0.887 | 0.756     | 0.894  | 0.819 | 0.771    | 0.781    | 0.776       |
+| 0.3064        | 3.8050 | 18500 | 0.4792          | 0.785    | 0.887 | 0.77      | 0.873  | 0.818 | 0.777    | 0.785    | 0.782       |
+| 0.3031        | 3.9079 | 19000 | 0.4738          | 0.786    | 0.889 | 0.791     | 0.835  | 0.813 | 0.782    | 0.786    | 0.785       |
+| 0.305         | 4.0107 | 19500 | 0.4908          | 0.786    | 0.889 | 0.784     | 0.848  | 0.814 | 0.78     | 0.786    | 0.784       |
+| 0.2806        | 4.1135 | 20000 | 0.5368          | 0.785    | 0.888 | 0.768     | 0.879  | 0.819 | 0.777    | 0.785    | 0.782       |
+| 0.2795        | 4.2164 | 20500 | 0.5228          | 0.786    | 0.889 | 0.789     | 0.838  | 0.813 | 0.781    | 0.786    | 0.785       |
+| 0.2835        | 4.3192 | 21000 | 0.5038          | 0.785    | 0.888 | 0.773     | 0.867  | 0.817 | 0.778    | 0.785    | 0.782       |
+| 0.2776        | 4.4220 | 21500 | 0.5283          | 0.784    | 0.887 | 0.791     | 0.831  | 0.81  | 0.78     | 0.784    | 0.783       |
+| 0.2912        | 4.5249 | 22000 | 0.5161          | 0.786    | 0.888 | 0.773     | 0.869  | 0.818 | 0.779    | 0.786    | 0.783       |
+| 0.2766        | 4.6277 | 22500 | 0.5228          | 0.785    | 0.888 | 0.784     | 0.845  | 0.813 | 0.78     | 0.785    | 0.783       |
+| 0.2778        | 4.7306 | 23000 | 0.5270          | 0.787    | 0.888 | 0.772     | 0.874  | 0.82  | 0.779    | 0.787    | 0.784       |
+| 0.271         | 4.8334 | 23500 | 0.5362          | 0.786    | 0.888 | 0.777     | 0.862  | 0.817 | 0.78     | 0.786    | 0.784       |
+| 0.2835        | 4.9362 | 24000 | 0.5245          | 0.787    | 0.888 | 0.775     | 0.868  | 0.819 | 0.781    | 0.787    | 0.785       |
+
+
+### Framework versions
+
+- Transformers 4.53.1
+- Pytorch 2.6.0+cu124
+- Datasets 2.14.4
+- Tokenizers 0.21.2

config.json

@@ -0,0 +1,36 @@
+{
+  "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": "fake",
+    "1": "real"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "fake": 0,
+    "real": 1
+  },
+  "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": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.53.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

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special_tokens_map.json

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+  "mask_token": {
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tokenizer_config.json

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