--- license: apache-2.0 base_model: albert-base-v2 tags: - generated_from_trainer metrics: - accuracy model-index: - name: training_dir results: [] --- # training_dir 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. It achieves the following results on the evaluation set: - Loss: 0.0393 - Accuracy: 0.9946 - F1 Score: 0.9946 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data SMS 1: Message: Hey, I'll be there in 10 minutes. See you soon! Label: label_0 (ham) SMS 2: Message: Congratulations! You've won a $1000 gift card. Claim it now by clicking the link. Label: label_1 (spam) 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. 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. The classification model uses these labels to identify and filter out spammy SMS messages, ensuring that legitimate messages reach the user's inbox (ham). ## Training procedure [Colab](https://colab.research.google.com/drive/1aCE5jBRlqN7KKBIuEjQ40mx3eOzPEfBd?usp=sharing) ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:| | No log | 1.0 | 244 | 0.1070 | 0.9785 | 0.9785 | | No log | 2.0 | 488 | 0.0673 | 0.9880 | 0.9880 | | 0.0885 | 3.0 | 732 | 0.0293 | 0.9946 | 0.9946 | | 0.0885 | 4.0 | 976 | 0.0280 | 0.9964 | 0.9964 | | 0.0306 | 5.0 | 1220 | 0.0355 | 0.9952 | 0.9952 | | 0.0306 | 6.0 | 1464 | 0.0364 | 0.9952 | 0.9952 | | 0.0087 | 7.0 | 1708 | 0.0448 | 0.9946 | 0.9946 | | 0.0087 | 8.0 | 1952 | 0.0618 | 0.9922 | 0.9922 | | 0.0047 | 9.0 | 2196 | 0.0420 | 0.9946 | 0.9946 | | 0.0047 | 10.0 | 2440 | 0.0393 | 0.9946 | 0.9946 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3