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scam-alert-distil-roberta

Text Classification
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roberta
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  1. README.md +99 -0
  2. model.safetensors +1 -1
  3. runs/Jun04_18-16-24_08cf44a5b839/events.out.tfevents.1717524985.08cf44a5b839.188.0 +2 -2
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: distilbert/distilroberta-base
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ - f1
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+ model-index:
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+ - name: scam-alert-distil-roberta
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # scam-alert-distil-roberta
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+
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+ This model is a fine-tuned version of [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.1213
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+ - Accuracy: 0.9861
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+ - F1: 0.9860
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 2e-05
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+ - train_batch_size: 8
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+ - eval_batch_size: 2
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 6
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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+ |:-------------:|:------:|:----:|:---------------:|:--------:|:------:|
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+ | No log | 0.1577 | 100 | 0.0852 | 0.9861 | 0.9860 |
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+ | No log | 0.3155 | 200 | 0.0690 | 0.9861 | 0.9858 |
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+ | No log | 0.4732 | 300 | 0.0965 | 0.9841 | 0.9842 |
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+ | No log | 0.6309 | 400 | 0.1015 | 0.9821 | 0.9818 |
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+ | No log | 0.7886 | 500 | 0.0629 | 0.9861 | 0.9859 |
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+ | No log | 0.9464 | 600 | 0.0788 | 0.9841 | 0.9839 |
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+ | No log | 1.1041 | 700 | 0.0500 | 0.9880 | 0.9880 |
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+ | No log | 1.2618 | 800 | 0.0778 | 0.9880 | 0.9879 |
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+ | No log | 1.4196 | 900 | 0.0657 | 0.9880 | 0.9879 |
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+ | No log | 1.5773 | 1000 | 0.1129 | 0.9841 | 0.9837 |
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+ | No log | 1.7350 | 1100 | 0.1038 | 0.9880 | 0.9879 |
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+ | No log | 1.8927 | 1200 | 0.0861 | 0.9880 | 0.9879 |
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+ | No log | 2.0505 | 1300 | 0.1047 | 0.9841 | 0.9841 |
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+ | No log | 2.2082 | 1400 | 0.0858 | 0.9900 | 0.9899 |
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+ | No log | 2.3659 | 1500 | 0.0936 | 0.9880 | 0.9879 |
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+ | No log | 2.5237 | 1600 | 0.0936 | 0.9861 | 0.9859 |
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+ | No log | 2.6814 | 1700 | 0.0909 | 0.9861 | 0.9859 |
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+ | No log | 2.8391 | 1800 | 0.1143 | 0.9841 | 0.9842 |
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+ | No log | 2.9968 | 1900 | 0.0902 | 0.9880 | 0.9881 |
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+ | No log | 3.1546 | 2000 | 0.0979 | 0.9841 | 0.9840 |
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+ | No log | 3.3123 | 2100 | 0.0977 | 0.9861 | 0.9860 |
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+ | No log | 3.4700 | 2200 | 0.0987 | 0.9861 | 0.9860 |
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+ | No log | 3.6278 | 2300 | 0.1016 | 0.9861 | 0.9860 |
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+ | No log | 3.7855 | 2400 | 0.1170 | 0.9861 | 0.9858 |
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+ | No log | 3.9432 | 2500 | 0.1106 | 0.9861 | 0.9859 |
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+ | 0.0267 | 4.1009 | 2600 | 0.1202 | 0.9861 | 0.9861 |
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+ | 0.0267 | 4.2587 | 2700 | 0.1207 | 0.9841 | 0.9841 |
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+ | 0.0267 | 4.4164 | 2800 | 0.1208 | 0.9841 | 0.9841 |
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+ | 0.0267 | 4.5741 | 2900 | 0.1215 | 0.9841 | 0.9841 |
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+ | 0.0267 | 4.7319 | 3000 | 0.1216 | 0.9841 | 0.9841 |
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+ | 0.0267 | 4.8896 | 3100 | 0.1215 | 0.9841 | 0.9841 |
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+ | 0.0267 | 5.0473 | 3200 | 0.1350 | 0.9861 | 0.9861 |
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+ | 0.0267 | 5.2050 | 3300 | 0.1165 | 0.9880 | 0.9880 |
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+ | 0.0267 | 5.3628 | 3400 | 0.1166 | 0.9880 | 0.9880 |
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+ | 0.0267 | 5.5205 | 3500 | 0.1167 | 0.9880 | 0.9880 |
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+ | 0.0267 | 5.6782 | 3600 | 0.1168 | 0.9880 | 0.9880 |
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+ | 0.0267 | 5.8360 | 3700 | 0.1212 | 0.9861 | 0.9860 |
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+ | 0.0267 | 5.9937 | 3800 | 0.1213 | 0.9861 | 0.9860 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.41.1
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+ - Pytorch 2.3.0+cu121
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+ - Datasets 2.19.2
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+ - Tokenizers 0.19.1
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