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---
license: mit
base_model: microsoft/mdeberta-v3-base
tags:
- generated_from_keras_callback
model-index:
- name: khadija69/debertav3_ASE_BIES
  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. -->

# khadija69/debertav3_ASE_BIES

This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6887
- Train Accuracy: 0.4877
- Validation Loss: 0.7630
- Validation Accuracy: 0.4497
- Epoch: 5

## 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': 'AdamWeightDecay', 'learning_rate': {'module': 'transformers.optimization_tf', 'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 700, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'warmup_steps': 500, 'power': 1.0, 'name': None}, 'registered_name': 'WarmUp'}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32

### Training results

| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 1.7487     | 0.2210         | 1.1266          | 0.3799              | 0     |
| 1.0173     | 0.4225         | 0.8678          | 0.4285              | 1     |
| 0.8657     | 0.4491         | 0.8170          | 0.4334              | 2     |
| 0.7807     | 0.4664         | 0.7785          | 0.4459              | 3     |
| 0.7304     | 0.4708         | 0.7662          | 0.4485              | 4     |
| 0.6887     | 0.4877         | 0.7630          | 0.4497              | 5     |


### Framework versions

- Transformers 4.41.2
- TensorFlow 2.15.0
- Datasets 2.19.2
- Tokenizers 0.19.1