Update README.md

README.md

@@ -6,197 +6,85 @@ library_name: peft
 # Model Card for Model ID
 
 <!-- Provide a quick summary of what the model is/does. -->
+base_model: Qwen/Qwen2.5-7B-Instruct
+library_name: peft
 
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-## Model Details
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-### Model Description
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-<!-- Provide a longer summary of what this model is. -->
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-- **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]
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-### Model Sources [optional]
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-<!-- Provide the basic links for the model. -->
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-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
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-## 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. -->
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-### Direct Use
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-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-[More Information Needed]
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-### Downstream Use [optional]
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-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-[More Information Needed]
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-### Out-of-Scope Use
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-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-[More Information Needed]
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-## Bias, Risks, and Limitations
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-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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-[More Information Needed]
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-### Recommendations
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-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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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.
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-## How to Get Started with the Model
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-Use the code below to get started with the model.
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-[More Information Needed]
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-## Training Details
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-### Training Data
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-<!-- 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. -->
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-[More Information Needed]
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-### Training Procedure
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-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### Preprocessing [optional]
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-[More Information Needed]
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-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-#### Speeds, Sizes, Times [optional]
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-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-[More Information Needed]
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-## Evaluation
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-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-<!-- This should link to a Dataset Card if possible. -->
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-[More Information Needed]
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-[More Information Needed]
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-#### Metrics
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-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-[More Information Needed]
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-### Results
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-[More Information Needed]
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-#### Summary
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-## Model Examination [optional]
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-<!-- Relevant interpretability work for the model goes here -->
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-[More Information Needed]
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-## Environmental Impact
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-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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-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).
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-- **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]
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-[More Information Needed]
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-### Compute Infrastructure
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-[More Information Needed]
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-#### Hardware
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-[More Information Needed]
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-#### Software
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-[More Information Needed]
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-## Citation [optional]
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-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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-**BibTeX:**
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-[More Information Needed]
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-**APA:**
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-[More Information Needed]
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-## Glossary [optional]
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-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-[More Information Needed]
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-## More Information [optional]
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-[More Information Needed]
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-## Model Card Authors [optional]
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-[More Information Needed]
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-## Model Card Contact
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-[More Information Needed]
-### Framework versions
+It should be noted that the MTIPA, TIPA, and training data for this model are all from Chinese, and support for other languages may not be sufficient. If you need to train a model specific to a particular language or for a general purpose, please refer to our paper and GitHub
+
+This model is developed by MTIPA for the first task of predicting spelling errors and correcting them. It was trained using the MTIPA method and the base model is Qwen2.5-7B-Instruct. You can use it like this: 
+
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+base_model = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen2.5-7B-Instruct",
+    trust_remote_code=True,
+    torch_dtype="auto",
+    device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct")
+
+model = PeftModel.from_pretrained(base_model, "LLMMINE/MTIPA-7B-PositionTask")
+def chat(text):
+    system = "纠正输入这段话中的错别字，以[{position: 字符位置, incorrect: 错误字符, correct: 纠正后的字符}, ...]形式给出，字符位置从1开始计数，如果全部正确，给出[]\n"
+    messages = [
+        {"role": "system", "content": system},
+        {"role": "user", "content": text}
+    ]
+    text_input = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True
+    )
+
+    # print("Input to model:")
+    # print(text_input)
+    model_inputs = tokenizer([text_input], return_tensors="pt").to(model.device)
+
+    generated_ids = model.generate(
+        **model_inputs,
+        max_new_tokens=512,
+        temperature=0.01,
+    )
+    generated_ids = [
+        output_ids[len(input_ids):]
+        for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+    ]
+
+    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    # print("Model response:")
+    # print(response)
+    return response
+
+def main():
+    print("命令行聊天程序已启动。输入您的文本，或输入 'exit' 退出。")
+    while True:
+        user_input = input("您: ")
+        if user_input.lower() in ['exit', 'quit']:
+            print("程序已退出。")
+            break
+        if not user_input.strip():
+            print("请输入文本。")
+            continue
+        response = chat(user_input)
+        print("回复:", response)
+
+if __name__ == '__main__':
+    main()
+```
+
+Input: 
+```
+花雨在镇上落了一整夜，这静寂的风暴覆盖了屋顶，堵住了房门，令露宿的动物窒息而死。如此多的花朵自天而降，天亮时大界小巷都覆上了一层绵密的花毯，人们得用铲子耙子清理出通道才能出殡。
+```
+Taken from One Hundred Years of Solitude (Cien Años de Soledad) And let `街` -> `界`
+
+Output:
+```
+[{"position": 56, "incorrect": "界", "correct": "街"}]
+```
+
+Github: https://github.com/FloatFrank/TIPA
+Paper: https://arxiv.org/abs/2411.17679
 
 - PEFT 0.12.0