English Text Classification
Collection
2 items • Updated
bert-base-Classification-news-or-informational-nft
This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0009
- Accuracy: 0.9998
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.043 | 1.0 | 1175 | 0.0082 | 0.9984 |
| 0.0151 | 2.0 | 2350 | 0.0038 | 0.9994 |
| 0.0053 | 3.0 | 3525 | 0.0009 | 0.9998 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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Model tree for dadashzadeh/bert-base-news-or-informational-nft-english
Base model
google-bert/bert-base-cased