Rishi-19/Profanity_Check_ClubbedData

1---2license: apache-2.03base_model: distilbert-base-uncased4tags:5- generated_from_keras_callback6model-index:7- name: Rishi-19/Profanity_Check_ClubbedData8  results: []9---10 11<!-- This model card has been generated automatically according to the information Keras had access to. You should12probably proofread and complete it, then remove this comment. -->13 14# Rishi-19/Profanity_Check_ClubbedData15 16This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.17It achieves the following results on the evaluation set:18- Train Loss: 0.029619- Validation Loss: 0.142520- Train Accuracy: 0.958521- Epoch: 322 23## Model description24 25More information needed26 27## Intended uses & limitations28 29More information needed30 31## Training and evaluation data32 33More information needed34 35## Training procedure36 37### Training hyperparameters38 39The following hyperparameters were used during training:40- 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': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 8935, '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}41- training_precision: float3242 43### Training results44 45| Train Loss | Validation Loss | Train Accuracy | Epoch |46|:----------:|:---------------:|:--------------:|:-----:|47| 0.1389     | 0.1092          | 0.9618         | 0     |48| 0.0782     | 0.1129          | 0.9583         | 1     |49| 0.0501     | 0.1208          | 0.9589         | 2     |50| 0.0296     | 0.1425          | 0.9585         | 3     |51 52 53### Framework versions54 55- Transformers 4.35.056- TensorFlow 2.14.057- Datasets 2.14.658- Tokenizers 0.14.159