Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+language:
+- en
+---
+
+# ChartInstruct: Instruction Tuning for Chart Comprehension and Reasoning
+
+Venue: **ACL 2024 (Findings)**
+
+Paper Link: https://arxiv.org/abs/2403.09028 
+
+The abstract of the paper states that: 
+> Charts provide visual representations of data and are widely used for analyzing information, addressing queries, and conveying insights to others. Various chart-related downstream tasks have emerged recently, such as question-answering and summarization. A common strategy to solve these tasks is to fine-tune various models originally trained on vision tasks language. However, such task-specific models are not capable of solving a wide range of chart-related tasks, constraining their real-world applicability. To overcome these challenges, we introduce ChartInstruct: a novel chart-specific vision-language Instruction following dataset comprising 191K instructions generated with 71K charts. We then present two distinct systems for instruction tuning on such datasets: (1) an end-to-end model that connects a vision encoder for chart understanding with a LLM; and (2) a pipeline model that employs a two-step approach to extract chart data tables and input them into the LLM. In experiments on four downstream tasks, we first show the effectiveness of our model--achieving a new set of state-of-the-art results. Further evaluation shows that our instruction-tuning approach supports a wide array of real-world chart comprehension and reasoning scenarios, thereby expanding the scope and applicability of our models to new kinds of tasks.
+# Web Demo
+If you wish to quickly try our model, you can access our public web demo hosted on the Hugging Face Spaces platform with a friendly interface!
+
+[ChartInstruct-FlanT5-XL Web Demo](https://huggingface.co/spaces/ahmed-masry/ChartInstruct-LLama2)
+
+# Inference
+You can easily use our models for inference with the huggingface library! 
+You just need to do the following:
+1. Chage the **_image_path_** to your chart example image path on your system
+2. Write the **_input_text_** 
+
+We recommend using beam search with a beam size of 4, but if your machine has low memory, you can remove the num_beams from the generate method. 
+```
+from PIL import Image
+import requests
+from transformers import AutoProcessor, AutoModelForSeq2SeqLM
+import torch
+
+torch.hub.download_url_to_file('https://raw.githubusercontent.com/vis-nlp/ChartQA/main/ChartQA%20Dataset/val/png/multi_col_1229.png', 'chart_example_1.png')
+
+image_path = "/content/chart_example_1.png"
+input_text = "What is the share of respondants who prefer Whatsapp in the 18-29 age group?"
+
+input_prompt = f"<image>\n Question: {input_text} Answer: "
+
+model = AutoModelForSeq2SeqLM.from_pretrained("ahmed-masry/ChartInstruct-FlanT5-XL", torch_dtype=torch.float16, trust_remote_code=True)
+processor = AutoProcessor.from_pretrained("ahmed-masry/ChartInstruct-FlanT5-XL")
+
+device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+model.to(device)
+
+image = Image.open(image_path).convert('RGB')
+
+inputs = processor(text=input_prompt, images=image, return_tensors="pt")
+inputs = {k: v.to(device) for k, v in inputs.items()}
+
+# change type if pixel_values in inputs to fp16. 
+inputs['pixel_values'] = inputs['pixel_values'].to(torch.float16)
+
+# Generate
+generate_ids = model.generate(**inputs, num_beams=4, max_new_tokens=512)
+output_text = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+print(output_text)
+
+```
+
+# Contact
+If you have any questions about this work, please contact **[Ahmed Masry](https://ahmedmasryku.github.io/)** using the following email addresses: **amasry17@ku.edu.tr** or **ahmed.elmasry24653@gmail.com**.
+
+# Reference
+Please cite our paper if you use our model in your research. 
+
+```
+@misc{masry2024chartinstruct,
+      title={ChartInstruct: Instruction Tuning for Chart Comprehension and Reasoning}, 
+      author={Ahmed Masry and Mehrad Shahmohammadi and Md Rizwan Parvez and Enamul Hoque and Shafiq Joty},
+      year={2024},
+      eprint={2403.09028},
+      archivePrefix={arXiv},
+      primaryClass={id='cs.CL' full_name='Computation and Language' is_active=True alt_name='cmp-lg' in_archive='cs' is_general=False description='Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.'}
+}
+```