End of training

README.md

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+---
+license: apache-2.0
+base_model: google-bert/bert-base-uncased
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: bert-agent-scam-classifier-v1.0
+  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. -->
+
+# bert-agent-scam-classifier-v1.0
+
+This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0039
+- Accuracy: {'accuracy': 0.996875}
+- Precision: {'precision': 0.996894409937888}
+- Recall: {'recall': 0.996875}
+- F1: {'f1': 0.9968749694821237}
+
+## 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: 20
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy               | Precision                        | Recall               | F1                         |
+|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:--------------------------------:|:--------------------:|:--------------------------:|
+| No log        | 1.0   | 160  | 0.0032          | {'accuracy': 0.996875} | {'precision': 0.996894409937888} | {'recall': 0.996875} | {'f1': 0.9968749694821237} |
+| No log        | 2.0   | 320  | 0.0033          | {'accuracy': 0.996875} | {'precision': 0.996894409937888} | {'recall': 0.996875} | {'f1': 0.9968749694821237} |
+| No log        | 3.0   | 480  | 0.0034          | {'accuracy': 0.996875} | {'precision': 0.996894409937888} | {'recall': 0.996875} | {'f1': 0.9968749694821237} |
+| 0.0354        | 4.0   | 640  | 0.0034          | {'accuracy': 0.996875} | {'precision': 0.996894409937888} | {'recall': 0.996875} | {'f1': 0.9968749694821237} |
+| 0.0354        | 5.0   | 800  | 0.0039          | {'accuracy': 0.996875} | {'precision': 0.996894409937888} | {'recall': 0.996875} | {'f1': 0.9968749694821237} |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1

model.safetensors

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