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---
license: mit
base_model: microsoft/deberta-v3-xsmall
tags:
- generated_from_keras_callback
model-index:
- name: rubakha/deberta
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. -->
# rubakha/deberta
This model is a fine-tuned version of [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1950
- Train Accuracy: 0.933
- Validation Loss: 0.2168
- Validation Accuracy: 0.9330
- Train Precision: 0.9356
- Train Recall: 0.933
- Train F1: 0.9326
- 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': 5000, '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 | Train Precision | Train Recall | Train F1 | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:---------------:|:------------:|:--------:|:-----:|
| 0.9796 | 0.8995 | 0.3173 | 0.8995 | 0.8999 | 0.8995 | 0.8989 | 0 |
| 0.2811 | 0.932 | 0.2170 | 0.9320 | 0.9343 | 0.932 | 0.9317 | 1 |
| 0.1950 | 0.933 | 0.2168 | 0.9330 | 0.9356 | 0.933 | 0.9326 | 2 |
### Framework versions
- Transformers 4.38.2
- TensorFlow 2.15.0
- Datasets 2.18.0
- Tokenizers 0.15.2
|