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bert-base-uncased-spam-classifier
pritihalder98/bert-base-uncased-spam-classifier
bert-base-uncased-spam-classifier is a text classification model from pritihalder98. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- bert
- Access
- Public
- Created
- Jun 8, 2023
- Updated
- Jun 9, 2023
- SHA
- 952163a1
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Model card
From the Hugging Face model README
<!-- 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. -->
bert-base-uncased-spam-classifier
This model is a fine-tuned version of nlptown/bert-base-multilingual-uncased-sentiment on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0895
- Accuracy: 0.9892
- Precision: 0.9580
- Recall: 0.9580
- F1: 0.9580
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.0858 | 1.0 | 1115 | 0.0873 | 0.9883 | 0.9577 | 0.9510 | 0.9544 |
| 0.0503 | 2.0 | 2230 | 0.0628 | 0.9910 | 0.9784 | 0.9510 | 0.9645 |
| 0.0249 | 3.0 | 3345 | 0.0939 | 0.9892 | 0.9712 | 0.9441 | 0.9574 |
| 0.0083 | 4.0 | 4460 | 0.0955 | 0.9892 | 0.9580 | 0.9580 | 0.9580 |
| 0.0 | 5.0 | 5575 | 0.0927 | 0.9892 | 0.9580 | 0.9580 | 0.9580 |
| 0.0 | 6.0 | 6690 | 0.0895 | 0.9892 | 0.9580 | 0.9580 | 0.9580 |
Framework versions
- Transformers 4.30.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- mit
- Created
- Jun 8, 2023
- Updated
- Jun 9, 2023