---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_keras_callback
model-index:
- name: vnktrmnb/xlm-roberta-base-FT-TyDiQA_AUQC
  results: []
---

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probably proofread and complete it, then remove this comment. -->

# vnktrmnb/xlm-roberta-base-FT-TyDiQA_AUQC

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.7971
- Train End Logits Accuracy: 0.5734
- Train Start Logits Accuracy: 0.6205
- Validation Loss: 0.5606
- Validation End Logits Accuracy: 0.8322
- Validation Start Logits Accuracy: 0.8713
- Epoch: 0

## 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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 4176, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32

### Training results

| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 1.7971     | 0.5734                    | 0.6205                      | 0.5606          | 0.8322                         | 0.8713                           | 0     |


### Framework versions

- Transformers 4.31.0
- TensorFlow 2.12.0
- Datasets 2.14.4
- Tokenizers 0.13.3