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README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: distilbert-base-uncased
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: distilbert-zim-phishing
+  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-zim-phishing
+
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6010
+- Accuracy: 0.75
+- Precision: 0.7059
+- Recall: 1.0
+- F1: 0.8276
+
+## 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: 4
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 20
+- num_epochs: 10
+- mixed_precision_training: Native AMP
+- label_smoothing_factor: 0.1
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 0.6881        | 1.0   | 17   | 0.6624          | 0.6      | 0.6       | 1.0    | 0.75   |
+| 0.5902        | 2.0   | 34   | 0.6293          | 0.6      | 0.6       | 1.0    | 0.75   |
+| 0.5222        | 3.0   | 51   | 0.5664          | 0.8      | 0.8333    | 0.8333 | 0.8333 |
+| 0.4697        | 4.0   | 68   | 0.6121          | 0.8      | 0.8333    | 0.8333 | 0.8333 |
+| 0.2758        | 5.0   | 85   | 0.7096          | 0.7      | 0.8       | 0.6667 | 0.7273 |
+| 0.2025        | 6.0   | 102  | 0.8511          | 0.7      | 0.8       | 0.6667 | 0.7273 |
+| 0.2784        | 7.0   | 119  | 0.8829          | 0.7      | 0.8       | 0.6667 | 0.7273 |
+| 0.2275        | 8.0   | 136  | 0.9294          | 0.6      | 0.6667    | 0.6667 | 0.6667 |
+| 0.2121        | 9.0   | 153  | 0.9439          | 0.6      | 0.6667    | 0.6667 | 0.6667 |
+| 0.2087        | 10.0  | 170  | 0.9450          | 0.6      | 0.6667    | 0.6667 | 0.6667 |
+
+
+### Framework versions
+
+- Transformers 5.0.0
+- Pytorch 2.10.0+cu128
+- Datasets 4.0.0
+- Tokenizers 0.22.2

config.json

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+{
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForSequenceClassification"
+  ],
+  "attention_dropout": 0.1,
+  "bos_token_id": null,
+  "dim": 768,
+  "dropout": 0.1,
+  "dtype": "float32",
+  "eos_token_id": null,
+  "hidden_dim": 3072,
+  "id2label": {
+    "0": "ham",
+    "1": "spam"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "ham": 0,
+    "spam": 1
+  },
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "pad_token_id": 0,
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.0.0",
+  "use_cache": false,
+  "vocab_size": 30522
+}

model.safetensors

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+oid sha256:f17a29611412663e08b3431c8f044cef8be95e9c9818dc072b769f821c9bd08d
+size 267832560

tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "is_local": false,
+  "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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+size 5201