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--- |
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license: apache-2.0 |
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datasets: |
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- yuan-tian/chartgpt-dataset |
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language: |
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- en |
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metrics: |
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- rouge |
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pipeline_tag: text2text-generation |
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base_model: |
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- google/flan-t5-xl |
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new_version: yuan-tian/chartgpt-llama3 |
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--- |
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# Model Card for ChartGPT |
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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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This model is used to generate charts from natural language. For more information, please refer to the paper. |
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* **Model type:** Language model |
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* **Language(s) (NLP)**: English |
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* **License**: Apache 2.0 |
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* **Finetuned from model**: [FLAN-T5-XL](https://huggingface.co/google/flan-t5-xl) |
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* **Research paper**: [ChartGPT: Leveraging LLMs to Generate Charts from Abstract Natural Language](https://ieeexplore.ieee.org/document/10443572) |
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### Model Input Format |
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<details> |
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<summary> Click to expand </summary> |
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Model input on the Step `x`. Specifically, `<...>` serves as a seperation token. |
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``` |
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{table name} |
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<head> {column names} |
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<type> {column types} |
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<data> {data row 1} <line> {data row 2} <line> |
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<utterance> {NL utterance} |
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<ans> |
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<sep> {Step 1 prompt} {Answer 2} |
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... |
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<sep> {Step x-1 prompt} {Answer x-1} |
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<sep> {Step x prompt} |
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``` |
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And the model should output the answer corresponding to step `x`. |
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The step 1-6 prompts are as follows: |
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``` |
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Step 1. Select columns: |
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Step 2. Add filter: |
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Step 3. Add aggregations: |
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Step 4. Select chart type: |
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Step 5. Choose encoding: |
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Step 6. Add sort: |
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``` |
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</details> |
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## How to Get Started with the Model |
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### Running the Model on a GPU |
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An example of a movie dataset with an utterance "What kinds of movies are the most popular?". |
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The model should give the answers to step 1 (select columns). |
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You can use the code below to test if you can run the model successfully. |
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<details> |
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<summary> Click to expand </summary> |
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```python |
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from transformers import ( |
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AutoTokenizer, |
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AutoModelForSeq2SeqLM, |
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) |
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tokenizer = AutoTokenizer.from_pretrained("yuan-tian/chartgpt") |
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model = AutoModelForSeq2SeqLM.from_pretrained("yuan-tian/chartgpt", device_map="auto") |
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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:" |
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inputs = tokenizer(input_text, return_tensors="pt", padding=True).to("cuda") |
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outputs = model.generate(**inputs) |
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print(tokenizer.decode(outputs[0], skip_special_tokens = True)) |
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``` |
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</details> |
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## Training Details |
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### Training Data |
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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). |
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### Training Procedure |
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Plan to update the preprocessing and training procedure in the future. |
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## Citation |
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**BibTeX:** |
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``` |
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@article{tian2024chartgpt, |
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title={ChartGPT: Leveraging LLMs to Generate Charts from Abstract Natural Language}, |
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author={Tian, Yuan and Cui, Weiwei and Deng, Dazhen and Yi, Xinjing and Yang, Yurun and Zhang, Haidong and Wu, Yingcai}, |
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journal={IEEE Transactions on Visualization and Computer Graphics}, |
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year={2024}, |
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pages={1-15}, |
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doi={10.1109/TVCG.2024.3368621} |
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} |
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``` |
