---
license: cc-by-sa-4.0
base_model: nlpaueb/legal-bert-small-uncased
tags:
- generated_from_keras_callback
model-index:
- name: bubai567/nifty_bert
  results: []
---

<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->

# bubai567/nifty_bert

This model is a fine-tuned version of [nlpaueb/legal-bert-small-uncased](https://huggingface.co/nlpaueb/legal-bert-small-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0529
- Train Accuracy: 0.9889
- Validation Loss: 0.0324
- Validation Accuracy: 0.9933
- Epoch: 1

## Model description

This model has been trained using 30 days of Nifty index data at 30-minute intervals. In this training dataset, signal values are represented as follows: 1 for peak signals, -1 for valley signals, and 0 for stay signals. The Smooth Z-Score method is employed to extract training samples. For example, a 30x10 input sample might look like this: 0 0 -1 -1 0 0 1 0 0 1. This model uses these samples to predict the direction of the Nifty index for the next 30 minutes.
## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 608, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32

### Training results

| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.1528     | 0.9651         | 0.0509          | 0.9914              | 0     |
| 0.0529     | 0.9889         | 0.0324          | 0.9933              | 1     |


### Framework versions

- Transformers 4.33.2
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.13.3