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---
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
** Model is not maintained, if you want latest stock/crypto/forex prediction using algo like PPO, A2C, transformers, you can contact me t.me/bbubai **
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
|