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---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: TenaliAI-FinTech-v1
results: []
---
<!-- 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. -->
# TenaliAI-FinTech-v1
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7505
## 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: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 1.6215 | 1.0 | 5485 | 1.3611 |
| 0.9667 | 2.0 | 10970 | 0.9202 |
| 0.7911 | 3.0 | 16455 | 0.8125 |
| 0.7406 | 4.0 | 21940 | 0.7648 |
| 0.6934 | 5.0 | 27425 | 0.7711 |
| 0.6457 | 6.0 | 32910 | 0.7546 |
| 0.6511 | 7.0 | 38395 | 0.7506 |
| 0.6244 | 8.0 | 43880 | 0.7548 |
| 0.6076 | 9.0 | 49365 | 0.7505 |
| 0.6003 | 10.0 | 54850 | 0.7579 |
| 0.5649 | 11.0 | 60335 | 0.7583 |
| 0.5616 | 12.0 | 65820 | 0.7597 |
| 0.5688 | 13.0 | 71305 | 0.7685 |
| 0.5571 | 14.0 | 76790 | 0.7705 |
| 0.5709 | 15.0 | 82275 | 0.7666 |
| 0.56 | 16.0 | 87760 | 0.7790 |
| 0.5474 | 17.0 | 93245 | 0.7798 |
| 0.5431 | 18.0 | 98730 | 0.7906 |
| 0.541 | 19.0 | 104215 | 0.7987 |
| 0.5426 | 20.0 | 109700 | 0.7953 |
| 0.5534 | 21.0 | 115185 | 0.7978 |
| 0.5541 | 22.0 | 120670 | 0.7929 |
| 0.538 | 23.0 | 126155 | 0.7997 |
| 0.5463 | 24.0 | 131640 | 0.8205 |
| 0.5606 | 25.0 | 137125 | 0.8226 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|