metadata

language:
  - ar
license: apache-2.0
base_model: nadsoft/hamsa-v0.1-beta
tags:
  - generated_from_trainer
datasets:
  - nadsoft/arabic-98
metrics:
  - wer
model-index:
  - name: hamsa-beta-v0.3Q
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: nadsoft/arabic-98
          type: nadsoft/arabic-98
        metrics:
          - name: Wer
            type: wer
            value: 19.302853050017905

hamsa-beta-v0.3Q

This model is a fine-tuned version of nadsoft/hamsa-v0.1-beta on the nadsoft/arabic-98 dataset. It achieves the following results on the evaluation set:

Loss: 0.2362
Wer Ortho: 21.12
Wer: 19.3029

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:

learning_rate: 1e-05
train_batch_size: 32
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
training_steps: 4000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer Ortho	Wer
0.2617	0.25	1000	0.2684	22.16	18.8134
0.227	0.5	2000	0.2565	18.6971	16.7482
0.2585	0.75	3000	0.2442	18.2400	16.3304
0.2632	1.0	4000	0.2362	21.12	19.3029

Framework versions

Transformers 4.36.2
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.0