TenaliAI-FinTech-v2
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0042
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: 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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 14 | 3.8371 |
| No log | 2.0 | 28 | 3.0541 |
| No log | 3.0 | 42 | 1.6426 |
| No log | 4.0 | 56 | 0.7499 |
| No log | 5.0 | 70 | 0.2611 |
| No log | 6.0 | 84 | 0.0687 |
| No log | 7.0 | 98 | 0.0198 |
| No log | 8.0 | 112 | 0.0085 |
| No log | 9.0 | 126 | 0.0055 |
| No log | 10.0 | 140 | 0.0042 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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Model tree for credentek/TenaliAI-FinTech-v2
Base model
google-bert/bert-base-uncased