| --- |
| 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 |
| |
