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--- |
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license: apache-2.0 |
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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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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: HamSpamBERT |
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results: [] |
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widget: |
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- text: "Ok i am on the way to home bye" |
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example_title: "Ham" |
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- text: "PRIVATE! Your 2004 Account Statement for 07742676969 shows 786 unredeemed Bonus Points. To claim call 08719180248 Identifier Code: 45239 Expires" |
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example_title: "Spam" |
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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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# HamSpamBERT |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0072 |
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- Accuracy: 0.9991 |
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- Precision: 1.0 |
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- Recall: 0.9933 |
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- F1: 0.9966 |
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```python |
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from transformers import pipeline, BertTokenizer, BertForSequenceClassification |
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tokenizer = BertTokenizer.from_pretrained("udit-k/HamSpamBERT") |
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model = BertForSequenceClassification.from_pretrained("udit-k/HamSpamBERT") |
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classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer) |
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print(classifier("Call this number to win FREE IPL FINAL tickets!!!")) |
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print(classifier("Call me when you reach home :)")) |
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``` |
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``` |
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[{'label': 'LABEL_1', 'score': 0.9999189376831055}] |
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[{'label': 'LABEL_0', 'score': 0.9999370574951172}] |
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``` |
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## Model description |
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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. |
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- LABEL_0 = Ham (Not spam) |
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- LABEL_1 = Spam |
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## Intended uses & limitations |
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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. |
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## Training and evaluation data |
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- Training corpus = 80% |
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- Evaluation corpus = 20% |
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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: 16 |
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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: 7 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| No log | 1.0 | 279 | 0.0492 | 0.9901 | 1.0 | 0.9262 | 0.9617 | |
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| 0.0635 | 2.0 | 558 | 0.0117 | 0.9982 | 1.0 | 0.9866 | 0.9932 | |
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| 0.0635 | 3.0 | 837 | 0.0120 | 0.9982 | 0.9933 | 0.9933 | 0.9933 | |
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| 0.0138 | 4.0 | 1116 | 0.0072 | 0.9991 | 1.0 | 0.9933 | 0.9966 | |
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| 0.0138 | 5.0 | 1395 | 0.0086 | 0.9982 | 0.9933 | 0.9933 | 0.9933 | |
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| 0.0007 | 6.0 | 1674 | 0.0090 | 0.9982 | 0.9933 | 0.9933 | 0.9933 | |
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| 0.0007 | 7.0 | 1953 | 0.0091 | 0.9982 | 0.9933 | 0.9933 | 0.9933 | |
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### Framework versions |
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- Transformers 4.30.0 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.13.3 |
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