Update README.md

README.md

@@ -1,202 +1,141 @@
 ---
 base_model: fnlp/bart-large-chinese
 library_name: peft
+pipeline_tag: summarization
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# Model Card for LoRA Fine-tuned Chinese BART
 
+这是一个基于 [`fnlp/bart-large-chinese`](https://huggingface.co/fnlp/bart-large-chinese) 模型进行 LoRA 微调的中文摘要模型，训练任务为中文新闻标题或摘要生成，适用于中文短文本压缩和提炼。
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
+本模型使用 PEFT 框架对 `fnlp/bart-large-chinese` 进行参数高效微调，采用了 LoRA（Low-Rank Adaptation）技术，仅调整注意力中的部分权重矩阵，使得训练过程更轻量。
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Developed by:** [Your Name or Organization]
+- **Model type:** Seq2Seq（BART）
+- **Language(s):** Chinese
+- **License:** Same as base model (assumed Apache 2.0, verify if needed)
+- **Finetuned from model:** fnlp/bart-large-chinese
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+可用于中文摘要任务，如新闻标题生成、内容压缩等。
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+不适用于多语言摘要、多文档总结或事实一致性要求极高的任务。
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+该模型基于公开中文数据集进行训练，可能在处理敏感内容、歧视性语言或特定社会群体时存在偏差。
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+建议仅在清洗干净的中文文本数据上使用该模型，避免用于决策支持或敏感领域。
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+from peft import PeftModel
 
-[More Information Needed]
+base_model = AutoModelForSeq2SeqLM.from_pretrained("fnlp/bart-large-chinese")
+peft_model = PeftModel.from_pretrained(base_model, "your-username/your-model-name")
+tokenizer = AutoTokenizer.from_pretrained("fnlp/bart-large-chinese")
+
+inputs = tokenizer("据报道，苹果将在下月发布新款iPhone。", return_tensors="pt")
+summary_ids = peft_model.generate(**inputs, max_length=30)
+print(tokenizer.decode(summary_ids[0], skip_special_tokens=True))
+````
 
 ## Training Details
 
 ### Training Data
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
+微调数据来自预处理后的中文新闻摘要数据集（如LCSTS），分为训练集与验证集，并使用了 `datasets` 库保存和加载。
 
 ### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
+#### Preprocessing
 
+* 使用 HuggingFace tokenizer 编码输入/输出文本
+* 设置 `max_source_length` 和 `max_target_length`
 
 #### 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]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+* **Epochs:** 4
+* **Batch Size:** 64
+* **Learning Rate:** 2e-5
+* **Evaluation Steps:** 5000
+* **Save Steps:** 10000
+* **Precision:** fp16
+* **LoRA Config:** r=8, alpha=16, dropout=0.1
+* **Target Modules:** `q_proj`, `v_proj`
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
+### Testing Data
 
-#### Factors
+使用与训练集相同来源的 held-out 验证集。
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+### Metrics
 
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+使用 ROUGE-1 / ROUGE-2 / ROUGE-L 评估自动摘要质量，中文评估按“字”或“词”颗粒度使用 `jieba` 分词。
 
 ### Results
 
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
+示例结果：
 
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+| Metric  | Score |
+| ------- | ----- |
+| ROUGE-1 | 0.35  |
+| ROUGE-2 | 0.19  |
+| ROUGE-L | 0.31  |
 
 ## 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]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+* **Hardware Type:** NVIDIA A100 GPU
+* **Hours used:** 2\~3 hours
+* **Cloud Provider:** \[Optional: e.g., Azure / AWS / 自有服务器]
+* **Compute Region:** \[Optional]
+* **Carbon Emitted:** Estimated using [MLCO2 calculator](https://mlco2.github.io/impact#compute)
 
-## Technical Specifications [optional]
+## Technical Specifications
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+基于 BART 的编码-解码结构，目标是最小化生成摘要与参考摘要之间的交叉熵损失。
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
+* **GPU:** NVIDIA A100 80GB
+* **Software:** PyTorch, Transformers, PEFT, Datasets, jieba
 
-[More Information Needed]
+## Citation
 
-## Citation [optional]
+```bibtex
+@misc{your2025bartlora,
+  title={LoRA Fine-tuned BART for Chinese Summarization},
+  author={Your Name},
+  year={2025},
+  howpublished={\url{https://huggingface.co/your-username/your-model-name}},
+}
+```
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
+## Model Card Contact
 
-## Model Card Authors [optional]
+* \[[your-email@example.com](mailto:your-email@example.com)]
 
-[More Information Needed]
+```
 
-## Model Card Contact
+---
 
-[More Information Needed]
-### Framework versions
+你可以根据需要替换 `"your-username/your-model-name"` 和 `"Your Name"` 以及邮箱等信息。
 
-- PEFT 0.11.1
+如果你还需要我帮你生成 [上传脚本](f) 或 [README 生成器](f)，可以告诉我。
+```