End of training

README.md

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+---
+license: apache-2.0
+base_model: distilbert/distilbert-base-uncased
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: distilbert-scam-classifier-v1.2
+  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-scam-classifier-v1.2
+
+This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0368
+- Accuracy: {'accuracy': 1.0}
+- Precision: {'precision': 1.0}
+- Recall: {'recall': 1.0}
+- F1: {'f1': 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: 2e-05
+- 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: 2
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy             | Precision                         | Recall             | F1                         |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------:|:---------------------------------:|:------------------:|:--------------------------:|
+| No log        | 1.0   | 40   | 0.1258          | {'accuracy': 0.9875} | {'precision': 0.9878048780487806} | {'recall': 0.9875} | {'f1': 0.9874980465697764} |
+| No log        | 2.0   | 80   | 0.0368          | {'accuracy': 1.0}    | {'precision': 1.0}                | {'recall': 1.0}    | {'f1': 1.0}                |
+
+
+### Framework versions
+
+- Transformers 4.40.1
+- Pytorch 2.2.1+cu121
+- Datasets 2.19.0
+- Tokenizers 0.19.1

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