| --- |
| license: apache-2.0 |
| datasets: |
| - yuan-tian/chartgpt-dataset |
| language: |
| - en |
| metrics: |
| - rouge |
| pipeline_tag: text2text-generation |
| base_model: |
| - google/flan-t5-xl |
| new_version: yuan-tian/chartgpt-llama3 |
| --- |
| # Model Card for ChartGPT |
|
|
| ## 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. |
|
|
| * **Model type:** Language model |
| * **Language(s) (NLP)**: English |
| * **License**: Apache 2.0 |
| * **Finetuned from model**: [FLAN-T5-XL](https://huggingface.co/google/flan-t5-xl) |
| * **Research paper**: [ChartGPT: Leveraging LLMs to Generate Charts from Abstract Natural Language](https://ieeexplore.ieee.org/document/10443572) |
|
|
| ### Model Input Format |
|
|
| <details> |
| <summary> Click to expand </summary> |
|
|
| Model input on the Step `x`. Specifically, `<...>` serves as a seperation token. |
|
|
| ``` |
| {table name} |
| <head> {column names} |
| <type> {column types} |
| <data> {data row 1} <line> {data row 2} <line> |
| <utterance> {NL utterance} |
| <ans> |
| <sep> {Step 1 prompt} {Answer 2} |
| ... |
| <sep> {Step x-1 prompt} {Answer x-1} |
| <sep> {Step x prompt} |
| ``` |
|
|
| And the model should output the answer corresponding to step `x`. |
|
|
| The step 1-6 prompts are as follows: |
|
|
| ``` |
| Step 1. Select columns: |
| Step 2. Add filter: |
| Step 3. Add aggregations: |
| Step 4. Select chart type: |
| Step 5. Choose encoding: |
| Step 6. Add sort: |
| ``` |
| </details> |
|
|
| ## How to Get Started with the Model |
|
|
| ### Running the Model on a GPU |
|
|
| An example of a movie dataset with an utterance "What kinds of movies are the most popular?". |
| The model should give the answers to step 1 (select columns). |
| 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, |
| AutoModelForSeq2SeqLM, |
| ) |
| tokenizer = AutoTokenizer.from_pretrained("yuan-tian/chartgpt") |
| model = AutoModelForSeq2SeqLM.from_pretrained("yuan-tian/chartgpt", device_map="auto") |
| input_text = "movies <head> Title,Worldwide_Gross,Production_Budget,Release_Year,Content_Rating,Running_Time,Major_Genre,Creative_Type,Rotten_Tomatoes_Rating,IMDB_Rating <type> nominal,quantitative,quantitative,temporal,nominal,quantitative,nominal,nominal,quantitative,quantitative <data> From Dusk Till Dawn,25728961,20000000,1996,R,107,Horror,Fantasy,63,7.1 <line> Broken Arrow,148345997,65000000,1996,R,108,Action,Contemporary Fiction,55,5.8 <line> <utterance> What kinds of movies are the most popular? <ans> <sep> Step 1. Select the columns:" |
| 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 model is Fine-tuned from [FLAN-T5-XL](https://huggingface.co/google/flan-t5-xl) on the [chartgpt-dataset](https://huggingface.co/datasets/yuan-tian/chartgpt-dataset). |
|
|
| ### Training Procedure |
|
|
| Plan to update the preprocessing and training procedure in the future. |
|
|
| ## Citation |
|
|
|
|
| **BibTeX:** |
|
|
| ``` |
| @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} |
| } |
| ``` |
