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---
language:
- zh
license: apache-2.0
base_model: whucedar/zh-CN-model
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- whucedar/retrain_jiaozhu_50
metrics:
- wer
model-index:
- name: zh-CN-2-model - whucedar
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: retrain_jiaozhu_50
type: whucedar/retrain_jiaozhu_50
args: 'config: zh, split: test'
metrics:
- name: Wer
type: wer
value: 13.333333333333334
---
<!-- 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. -->
# zh-CN-2-model - whucedar
This model is a fine-tuned version of [whucedar/zh-CN-model](https://huggingface.co/whucedar/zh-CN-model) on the retrain_jiaozhu_50 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0166
- Wer: 13.3333
## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 200
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 0.0001 | 33.3333 | 100 | 0.0166 | 13.3333 |
| 0.0 | 66.6667 | 200 | 0.0166 | 13.3333 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.1+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1
|