HamSpamBERT

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HamSpamBERT

This model is a fine-tuned version of bert-base-uncased on 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

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 model on 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

Framework versions

• Transformers 4.30.0
• Pytorch 2.1.2
• Datasets 2.18.0
• Tokenizers 0.13.3