asr_v2_step_16000

# Model Card for Model ID

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## Model Details

### Model Description

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## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

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## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary

## Model Examination [optional]

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## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]

### Model Architecture and Objective

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#### Software

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## Citation [optional]

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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### Framework versions

- PEFT 0.15.2

# 模型卡片（Model Card）
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## 模型详情
### 模型描述
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- **开发者：** [需补充更多信息]
- **资助方（可选）：** [需补充更多信息]
- **分享方（可选）：** [需补充更多信息]
- **模型类型：** [需补充更多信息]
- **自然语言处理支持语言：** [需补充更多信息]
- **授权协议：** [需补充更多信息]
- **基于以下模型微调（可选）：** [需补充更多信息]

### 模型来源（可选）
<!-- 提供该模型的基础链接 -->

- **代码仓库：** [需补充更多信息]
- **论文（可选）：** [需补充更多信息]
- **演示（可选）：** [需补充更多信息]

## 模型用途
<!-- 解答有关模型预期使用场景的问题，包括可预见的模型使用者与受模型影响的群体 -->

### 直接使用
<!-- 本节说明未经过微调或未接入更大生态系统/应用的模型使用方式 -->

[需补充更多信息]

### 下游使用（可选）
<!-- 本节说明为特定任务微调后的模型使用方式，或接入更大生态系统/应用的模型使用方式 -->

[需补充更多信息]

### 超出适用范围的使用
<!-- 本节说明不当使用、恶意使用，以及模型无法良好适配的应用场景 -->

[需补充更多信息]

## 偏差、风险与局限性
<!-- 本节旨在说明技术与社会技术层面的局限性 -->

[需补充更多信息]

### 建议
<!-- 本节旨在针对模型的偏差、风险与技术局限性给出建议 -->

用户（包括直接使用者与下游使用者）应知晓该模型存在的风险、偏差与局限性。如需制定进一步的相关建议，需补充更多信息。

## 模型快速上手指南
可通过以下代码快速上手该模型。

[需补充更多信息]

## 训练详情
### 训练数据
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### 训练流程
<!-- 本节与技术规格关联紧密，相关内容应在合适时链接至对应章节 -->

#### 预处理（可选）
[需补充更多信息]

#### 训练超参数
- **训练策略：** [需补充更多信息] <!--fp32、fp16混合精度、bf16混合精度、bf16非混合精度、fp16非混合精度、fp8混合精度 -->

#### 训练速度、模型规模与耗时（可选）
<!-- 本节提供吞吐量、起止时间、相关检查点大小等信息 -->

[需补充更多信息]

## 模型评估
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### 测试数据、影响因素与评估指标
#### 测试数据
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[需补充更多信息]

#### 影响因素
<!-- 此处为评估时的拆分维度，例如子群体或应用领域 -->

[需补充更多信息]

#### 评估指标
<!-- 此处为所使用的评估指标，理想情况下应附带选择该指标的原因说明 -->

[需补充更多信息]

### 评估结果
[需补充更多信息]

#### 总结

## 模型可解释性分析（可选）
<!-- 此处放置与模型可解释性相关的研究内容 -->

[需补充更多信息]

## 环境影响
<!-- 此处应填写总碳排放量（以克CO₂eq为单位）与其他相关考量因素，例如电力使用情况。请编辑以下示例文本 -->

碳排放量可通过[Lacoste等人(2019)](https://arxiv.org/abs/1910.09700)中提出的[机器学习影响计算器（Machine Learning Impact calculator）](https://mlco2.github.io/impact#compute)进行估算。

- **硬件类型：** [需补充更多信息]
- **使用时长：** [需补充更多信息]
- **云服务提供商：** [需补充更多信息]
- **计算区域：** [需补充更多信息]
- **碳排放量：** [需补充更多信息]

## 技术规格（可选）
### 模型架构与训练目标
[需补充更多信息]

### 计算基础设施
[需补充更多信息]

#### 硬件
[需补充更多信息]

#### 软件
[需补充更多信息]

## 引用信息（可选）
<!-- 若存在介绍该模型的论文或博客文章，此处应填写其APA与Bibtex格式的引用信息 -->

**BibTeX格式：**

[需补充更多信息]

**APA格式：**

[需补充更多信息]

## 术语表（可选）
<!-- 若有需要，可在此处添加有助于读者理解模型或模型卡片的术语与计算公式 -->

[需补充更多信息]

## 更多信息（可选）
[需补充更多信息]

## 模型卡片编写者（可选）
[需补充更多信息]

## 模型卡片联系人
[需补充更多信息]

### 框架版本
- PEFT 0.15.2