TenaliAI-FinTech-v1
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: 0.4091
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 | 221 | 2.9595 |
| No log | 2.0 | 442 | 1.8433 |
| 2.9917 | 3.0 | 663 | 1.1818 |
| 2.9917 | 4.0 | 884 | 0.8031 |
| 1.167 | 5.0 | 1105 | 0.5922 |
| 1.167 | 6.0 | 1326 | 0.5013 |
| 0.5816 | 7.0 | 1547 | 0.4465 |
| 0.5816 | 8.0 | 1768 | 0.4221 |
| 0.5816 | 9.0 | 1989 | 0.4091 |
| 0.433 | 10.0 | 2210 | 0.4123 |
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-v1
Base model
google-bert/bert-base-uncased