End of training

README.md

@@ -1,17 +1,71 @@
 ---
+library_name: transformers
 license: apache-2.0
+base_model: google-bert/bert-base-uncased
+tags:
+- generated_from_trainer
 metrics:
 - accuracy
-- roc_auc
-pipeline_tag: text-classification
-library_name: transformers
-base_model: google-bert/bert-base-uncased
-datasets:
-- shawhin/phishing-site-classification
+model-index:
+- name: bert-base-uncased_phising-classifier_teacher
+  results: []
 ---
-# Phishing URL Classifier (Teacher Model)
-Teacher model for knowledge distillation example.
 
-Video: coming soon! <br>
-Blog: coming soon! <br>
-Example code: coming soon!
+<!-- 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. -->
+
+# bert-base-uncased_phising-classifier_teacher
+
+This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2881
+- Accuracy: 0.867
+- Auc: 0.951
+
+## 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: 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 | Accuracy | Auc   |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----:|
+| 0.4916        | 1.0   | 263  | 0.4228          | 0.784    | 0.915 |
+| 0.3894        | 2.0   | 526  | 0.3586          | 0.818    | 0.932 |
+| 0.3837        | 3.0   | 789  | 0.3144          | 0.86     | 0.939 |
+| 0.3574        | 4.0   | 1052 | 0.4494          | 0.807    | 0.942 |
+| 0.3517        | 5.0   | 1315 | 0.3287          | 0.86     | 0.947 |
+| 0.3518        | 6.0   | 1578 | 0.3042          | 0.871    | 0.949 |
+| 0.3185        | 7.0   | 1841 | 0.2900          | 0.862    | 0.949 |
+| 0.3267        | 8.0   | 2104 | 0.2958          | 0.876    | 0.95  |
+| 0.3153        | 9.0   | 2367 | 0.2881          | 0.867    | 0.951 |
+| 0.3061        | 10.0  | 2630 | 0.2963          | 0.873    | 0.951 |
+
+
+### Framework versions
+
+- Transformers 4.44.2
+- Pytorch 2.2.2
+- Datasets 2.21.0
+- Tokenizers 0.19.1

special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

@@ -0,0 +1,55 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

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