Update README.md

README.md

@@ -1,199 +1,143 @@
 ---
-library_name: transformers
-tags: []
+license: apache-2.0
+datasets:
+- yuan-tian/chartgpt-dataset
+language:
+- en
+metrics:
+- rouge
+pipeline_tag: text2text-generation
 ---
-
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+# Model Card for ChartGPT-Llama3
 
 ## Model Details
 
 ### Model Description
 
 <!-- Provide a longer summary of what this model is. -->
+This model is used to generate charts from natural language. For more information, please refer to the paper. 
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **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. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
+* **Model type:** Language model
+* **Language(s) (NLP)**: English
+* **License**: Apache 2.0
+* **Finetuned from model**: [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
+* **Research paper**: [ChartGPT: Leveraging LLMs to Generate Charts from Abstract Natural Language](https://ieeexplore.ieee.org/document/10443572)
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+### Model Input Format
 
-### Direct Use
+<details>
+<summary> Click to expand </summary>
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+Model input on the Step `x`.
 
-[More Information Needed]
+```
+Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+Your response should follow the following format:
+{Step 1 prompt}
 
-### Downstream Use [optional]
+{Step x-1 prompt}
+{Step x prompt}
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+### Instruction:
+{instruction}
 
-[More Information Needed]
+### Input:
+Table Name: {table name}
+Table Header: {column names}
+Table Header Type: {column types}
+Table Data Example:
+{data row 1}
+{data row 2}
+Previous Answer:
+{previous answer}
 
-### Out-of-Scope Use
+### Response:
+```
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+And the model should output the answer corresponding to step `x`.  
 
-[More Information Needed]
+The step 1-6 prompts are as follows: 
 
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### 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.
+```
+Step 1. Select the columns:
+Step 2. Filter the data:
+Step 3. Add aggregate functions:
+Step 4. Choose chart type:
+Step 5. Select encodings:
+Step 6. Sort the data:
+```
+</details>
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[More Information Needed]
+### Running the Model on a GPU
+
+An example of a movie dataset with an instruction "Give me a visual representation of the faculty members by their professional status.". 
+The model should give the answers to all steps. 
+You can use the code below to test if you can run the model successfully. 
+
+<details>
+<summary> Click to expand </summary>
+
+```python
+from transformers import (
+    AutoTokenizer,
+    AutoModelForCausalLM,
+)
+tokenizer = AutoTokenizer.from_pretrained("yuan-tian/chartgpt-llama3")
+model = AutoModelForCausalLM.from_pretrained("yuan-tian/chartgpt-llama3", device_map="auto")
+input_text = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+Your response should follow the following format:
+Step 1. Select the columns:
+Step 2. Filter the data:
+Step 3. Add aggregate functions:
+Step 4. Choose chart type:
+Step 5. Select encodings:
+Step 6. Sort the data:
+
+### Instruction:
+Give me a visual representation of the faculty members by their professional status.
+
+### Input:
+Table Name: Faculty
+Table Header: FacID,Lname,Fname,Rank,Sex,Phone,Room,Building
+Table Header Type: quantitative,nominal,nominal,nominal,nominal,quantitative,nominal,nominal
+Table Data Example:
+1082,Giuliano,Mark,Instructor,M,2424,224,NEB
+1121,Goodrich,Michael,Professor,M,3593,219,NEB
+Previous Answer:
+
+
+### Response:"""
+inputs = tokenizer(input_text, return_tensors="pt", padding=True).to("cuda")
+outputs = model.generate(**inputs)
+print(tokenizer.decode(outputs[0], skip_special_tokens = True))
+```
+
+</details>
 
 ## 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]
-
-### 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]
-
-
-#### 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]
-
-## 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]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+This model is Fine-tuned from [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the [chartgpt-dataset](https://huggingface.co/datasets/yuan-tian/chartgpt-dataset). 
 
-[More Information Needed]
+### Training Procedure 
 
-#### Metrics
+Plan to update the preprocessing and training procedure in the future. 
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+## Citation
 
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## 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]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- 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 Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+```
+@article{tian2024chartgpt,
+  title={ChartGPT: Leveraging LLMs to Generate Charts from Abstract Natural Language},
+  author={Tian, Yuan and Cui, Weiwei and Deng, Dazhen and Yi, Xinjing and Yang, Yurun and Zhang, Haidong and Wu, Yingcai},
+  journal={IEEE Transactions on Visualization and Computer Graphics},
+  year={2024},
+  pages={1-15},
+  doi={10.1109/TVCG.2024.3368621}
+}
+```