End of training

README.md

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+---
+library_name: transformers
+base_model: microsoft/codebert-base
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+model-index:
+- name: Vulnerability_Detection_Using_CodeBERT
+  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. -->
+
+# Vulnerability_Detection_Using_CodeBERT
+
+This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0740
+- Accuracy: 1.0
+- Auc: 1.0
+- Precision: 1.0
+
+## 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: 0.0002
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- 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: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Auc | Precision |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---:|:---------:|
+| 0.2971        | 1.0   | 26   | 0.1815          | 0.925    | 1.0 | 0.81      |
+| 0.2407        | 2.0   | 52   | 0.1349          | 0.981    | 1.0 | 0.944     |
+| 0.2619        | 3.0   | 78   | 0.1668          | 0.887    | 1.0 | 0.739     |
+| 0.2207        | 4.0   | 104  | 0.1081          | 1.0      | 1.0 | 1.0       |
+| 0.1543        | 5.0   | 130  | 0.1037          | 0.981    | 1.0 | 1.0       |
+| 0.1428        | 6.0   | 156  | 0.0974          | 0.981    | 1.0 | 0.944     |
+| 0.1598        | 7.0   | 182  | 0.0916          | 0.981    | 1.0 | 1.0       |
+| 0.1324        | 8.0   | 208  | 0.1024          | 0.981    | 1.0 | 0.944     |
+| 0.1445        | 9.0   | 234  | 0.0726          | 1.0      | 1.0 | 1.0       |
+| 0.1287        | 10.0  | 260  | 0.0740          | 1.0      | 1.0 | 1.0       |
+
+
+### Framework versions
+
+- Transformers 4.50.0
+- Pytorch 2.6.0+cu124
+- Datasets 3.5.0
+- Tokenizers 0.21.1

config.json

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+{
+  "architectures": [
+    "RobertaForSequenceClassification"
+  ],
+  "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": "pas vulnerable",
+    "1": "vulnerable"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "pas vulnerable": 0,
+    "vulnerable": 1
+  },
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "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": "4.50.0",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 50265
+}

model.safetensors

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runs/Mar30_04-30-43_fe78b26bd799/events.out.tfevents.1743309129.fe78b26bd799.602.1

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special_tokens_map.json

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+{
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+    "lstrip": false,
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+    "rstrip": false,
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+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
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+}

tokenizer_config.json

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+  "add_prefix_space": false,
+  "added_tokens_decoder": {
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+  "clean_up_tokenization_spaces": false,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "RobertaTokenizer",
+  "trim_offsets": true,
+  "unk_token": "<unk>"
+}

training_args.bin

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