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---
language:
- ar
license: apache-2.0
base_model: nadsoft/hamsa-v0.1-beta
tags:
- generated_from_trainer
datasets:
- nadsoft/nadsoft-meetings
metrics:
- wer
model-index:
- name: Hamsa-meetings
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: nadsoft/nadsoft-meetings
type: nadsoft/nadsoft-meetings
metrics:
- name: Wer
type: wer
value: 43.449519230769226
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Hamsa-meetings
This model is a fine-tuned version of [nadsoft/hamsa-v0.1-beta](https://huggingface.co./nadsoft/hamsa-v0.1-beta) on the nadsoft/nadsoft-meetings dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9346
- Wer Ortho: 43.4495
- Wer: 43.4495
## 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: 2000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:|
| 0.4883 | 2.91 | 250 | 0.6170 | 40.2644 | 40.2644 |
| 0.1678 | 5.81 | 500 | 0.6893 | 43.6899 | 43.6899 |
| 0.0749 | 8.72 | 750 | 0.7367 | 42.0673 | 42.0673 |
| 0.0352 | 11.63 | 1000 | 0.7829 | 42.6683 | 42.6683 |
| 0.0214 | 14.53 | 1250 | 0.8553 | 43.9904 | 43.9904 |
| 0.0146 | 17.44 | 1500 | 0.9061 | 43.3894 | 43.3894 |
| 0.0112 | 20.35 | 1750 | 0.9225 | 44.2909 | 44.2909 |
| 0.0104 | 23.26 | 2000 | 0.9346 | 43.4495 | 43.4495 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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