| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - precision |
| - recall |
| - f1 |
| model-index: |
| - name: HamSpamBERT |
| results: [] |
| widget: |
| - text: "Ok i am on the way to home bye" |
| example_title: "Ham" |
| - text: "PRIVATE! Your 2004 Account Statement for 07742676969 shows 786 unredeemed Bonus Points. To claim call 08719180248 Identifier Code: 45239 Expires" |
| example_title: "Spam" |
| --- |
| |
| <!-- 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. --> |
|
|
| # HamSpamBERT |
|
|
| This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on [Spam-Ham](https://huggingface.co/datasets/SalehAhmad/Spam-Ham) dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0072 |
| - Accuracy: 0.9991 |
| - Precision: 1.0 |
| - Recall: 0.9933 |
| - F1: 0.9966 |
|
|
| ```python |
| from transformers import pipeline, BertTokenizer, BertForSequenceClassification |
| |
| tokenizer = BertTokenizer.from_pretrained("udit-k/HamSpamBERT") |
| model = BertForSequenceClassification.from_pretrained("udit-k/HamSpamBERT") |
| |
| classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer) |
| print(classifier("Call this number to win FREE IPL FINAL tickets!!!")) |
| print(classifier("Call me when you reach home :)")) |
| ``` |
| ``` |
| [{'label': 'LABEL_1', 'score': 0.9999189376831055}] |
| [{'label': 'LABEL_0', 'score': 0.9999370574951172}] |
| ``` |
|
|
| ## Model description |
|
|
| This model is a fine-tuned version of the [BERT](https://huggingface.co/bert-base-uncased) model on [Spam-Ham](https://huggingface.co/datasets/SalehAhmad/Spam-Ham) dataset to improve the performance of sentiment analysis on Spam Detection tasks. |
| - LABEL_0 = Ham (Not spam) |
| - LABEL_1 = Spam |
|
|
| ## Intended uses & limitations |
|
|
| This model can be used to detect spam texts. The primary limitation of this model is that it was trained on a corpus of about 4700 rows and evaluated on around 1200 rows. |
|
|
| ## Training and evaluation data |
|
|
| - Training corpus = 80% |
| - Evaluation corpus = 20% |
|
|
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 5e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 7 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
| | No log | 1.0 | 279 | 0.0492 | 0.9901 | 1.0 | 0.9262 | 0.9617 | |
| | 0.0635 | 2.0 | 558 | 0.0117 | 0.9982 | 1.0 | 0.9866 | 0.9932 | |
| | 0.0635 | 3.0 | 837 | 0.0120 | 0.9982 | 0.9933 | 0.9933 | 0.9933 | |
| | 0.0138 | 4.0 | 1116 | 0.0072 | 0.9991 | 1.0 | 0.9933 | 0.9966 | |
| | 0.0138 | 5.0 | 1395 | 0.0086 | 0.9982 | 0.9933 | 0.9933 | 0.9933 | |
| | 0.0007 | 6.0 | 1674 | 0.0090 | 0.9982 | 0.9933 | 0.9933 | 0.9933 | |
| | 0.0007 | 7.0 | 1953 | 0.0091 | 0.9982 | 0.9933 | 0.9933 | 0.9933 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.30.0 |
| - Pytorch 2.1.2 |
| - Datasets 2.18.0 |
| - Tokenizers 0.13.3 |
|
|
