File size: 2,936 Bytes
2c9d5de 5d1ecfe 2c9d5de 2f0ac5d 2c9d5de 2f0ac5d 2c9d5de |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 |
---
license: apache-2.0
base_model: albert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: training_dir
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. -->
# 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
|