File size: 1,974 Bytes
a5fb7d3 769c925 a5fb7d3 769c925 a5fb7d3 769c925 a5fb7d3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 |
---
license: mit
base_model: Davlan/xlm-roberta-base-finetuned-arabic
tags:
- generated_from_keras_callback
model-index:
- name: betteib/xlm-mlm-tn
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. -->
# betteib/xlm-mlm-tn
This model is a fine-tuned version of [Davlan/xlm-roberta-base-finetuned-arabic](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-arabic) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 8.1277
- Train Accuracy: 0.0048
- Validation Loss: 8.1367
- Validation Accuracy: 0.0046
- Epoch: 1
## 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': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 0.0001, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0001, 'decay_steps': 118, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 6, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.001}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 8.7004 | 0.0046 | 8.1656 | 0.0046 | 0 |
| 8.1277 | 0.0048 | 8.1367 | 0.0046 | 1 |
### Framework versions
- Transformers 4.31.0
- TensorFlow 2.12.0
- Datasets 2.19.1
- Tokenizers 0.13.3
|