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Training in progress epoch 2
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---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: showvikdbz/toxifcity-classifier-tf
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
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# showvikdbz/toxifcity-classifier-tf
This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co./google-bert/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0161
- Validation Loss: 0.0759
- Train Accuracy: 0.9835
- Train F1: 0.9684
- Train Precision: 0.9742
- Train Recall: 0.9627
- Epoch: 2
## 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:
- 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': 23256, '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 | Validation Loss | Train Accuracy | Train F1 | Train Precision | Train Recall | Epoch |
|:----------:|:---------------:|:--------------:|:--------:|:---------------:|:------------:|:-----:|
| 0.0951 | 0.0606 | 0.9825 | 0.9661 | 0.9823 | 0.9504 | 0 |
| 0.0406 | 0.0665 | 0.9828 | 0.9667 | 0.9882 | 0.9460 | 1 |
| 0.0161 | 0.0759 | 0.9835 | 0.9684 | 0.9742 | 0.9627 | 2 |
### Framework versions
- Transformers 4.44.2
- TensorFlow 2.17.0
- Datasets 2.21.0
- Tokenizers 0.19.1