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--- |
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license: apache-2.0 |
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base_model: albert-base-v2 |
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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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model-index: |
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- name: training_dir |
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results: [] |
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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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# training_dir |
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This model is a fine-tuned version of [albert-base-v2](https://huggingface.co./albert-base-v2) on an [Spam Data Collection](https://www.kaggle.com/datasets/abhishek14398/sms-spam-collection) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0393 |
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- Accuracy: 0.9946 |
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- F1 Score: 0.9946 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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SMS 1: |
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Message: Hey, I'll be there in 10 minutes. See you soon! |
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Label: label_0 (ham) |
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SMS 2: |
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Message: Congratulations! You've won a $1000 gift card. Claim it now by clicking the link. |
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Label: label_1 (spam) |
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In this SMS classification example, the first message is labeled as "label_0" because it appears to be a legitimate text message (ham) with someone informing they will arrive shortly. |
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The second message is labeled as "label_1" because it is clearly spam, offering a prize and urging the recipient to click a link, which is a common characteristic of spam messages. |
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The classification model uses these labels to identify and filter out spammy SMS messages, ensuring that legitimate messages reach the user's inbox (ham). |
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## Training procedure |
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[Colab](https://colab.research.google.com/drive/1aCE5jBRlqN7KKBIuEjQ40mx3eOzPEfBd?usp=sharing) |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 64 |
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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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:| |
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| No log | 1.0 | 244 | 0.1070 | 0.9785 | 0.9785 | |
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| No log | 2.0 | 488 | 0.0673 | 0.9880 | 0.9880 | |
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| 0.0885 | 3.0 | 732 | 0.0293 | 0.9946 | 0.9946 | |
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| 0.0885 | 4.0 | 976 | 0.0280 | 0.9964 | 0.9964 | |
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| 0.0306 | 5.0 | 1220 | 0.0355 | 0.9952 | 0.9952 | |
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| 0.0306 | 6.0 | 1464 | 0.0364 | 0.9952 | 0.9952 | |
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| 0.0087 | 7.0 | 1708 | 0.0448 | 0.9946 | 0.9946 | |
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| 0.0087 | 8.0 | 1952 | 0.0618 | 0.9922 | 0.9922 | |
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| 0.0047 | 9.0 | 2196 | 0.0420 | 0.9946 | 0.9946 | |
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| 0.0047 | 10.0 | 2440 | 0.0393 | 0.9946 | 0.9946 | |
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### Framework versions |
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- Transformers 4.33.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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