Create README.md

README.md

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+---
+language:
+  - en
+  - fr
+  - ro
+  - de
+  - multilingual
+pipeline_tag: image-to-text
+tags:
+  - image-captioning
+license: apache-2.0
+---
+# Model card for DePlot
+
+![pull_figure](https://s3.amazonaws.com/moonup/production/uploads/62441d1d9fdefb55a0b7d12c/u8rWTawSyUegF4jzwOpNO.png)
+
+
+#  Table of Contents
+
+0. [TL;DR](#TL;DR)
+1. [Using the model](#using-the-model)
+2. [Contribution](#contribution)
+3. [Citation](#citation)
+
+# TL;DR
+
+The abstract of the paper states that: 
+
+> Visual language such as charts and plots is ubiquitous in the human world. Comprehending plots and charts requires strong reasoning skills. Prior state-of-the-art (SOTA) models require at least tens of thousands of training examples and their reasoning capabilities are still much limited, especially on complex human-written queries. This paper presents the first one-shot solution to visual language reasoning. We decompose the challenge of visual language reasoning into two steps: (1) plot-to-text translation, and (2) reasoning over the translated text. The key in this method is a modality conversion module, named as DePlot, which translates the image of a plot or chart to a linearized table. The output of DePlot can then be directly used to prompt a pretrained large language model (LLM), exploiting the few-shot reasoning capabilities of LLMs. To obtain DePlot, we standardize the plot-to-table task by establishing unified task formats and metrics, and train DePlot end-to-end on this task. DePlot can then be used off-the-shelf together with LLMs in a plug-and-play fashion. Compared with a SOTA model finetuned on more than >28k data points, DePlot+LLM with just one-shot prompting achieves a 24.0% improvement over finetuned SOTA on human-written queries from the task of chart QA.
+
+
+# Using the model 
+
+## Converting from T5x to huggingface
+
+You can use the [`convert_pix2struct_checkpoint_to_pytorch.py`](https://github.com/huggingface/transformers/blob/main/src/transformers/models/pix2struct/convert_pix2struct_original_pytorch_to_hf.py) script as follows:
+```bash
+python convert_pix2struct_checkpoint_to_pytorch.py --t5x_checkpoint_path PATH_TO_T5X_CHECKPOINTS --pytorch_dump_path PATH_TO_SAVE --is_vqa
+```
+if you are converting a large model, run:
+```bash
+python convert_pix2struct_checkpoint_to_pytorch.py --t5x_checkpoint_path PATH_TO_T5X_CHECKPOINTS --pytorch_dump_path PATH_TO_SAVE --use-large --is_vqa
+```
+Once saved, you can push your converted model with the following snippet:
+```python
+from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor
+
+model = Pix2StructForConditionalGeneration.from_pretrained(PATH_TO_SAVE)
+processor = Pix2StructProcessor.from_pretrained(PATH_TO_SAVE)
+
+model.push_to_hub("USERNAME/MODEL_NAME")
+processor.push_to_hub("USERNAME/MODEL_NAME")
+```
+
+## Run a prediction
+
+You can run a prediction by querying an input image together with a question as follows:
+```python
+from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor
+import requests
+from PIL import Image
+
+model = Pix2StructForConditionalGeneration.from_pretrained('google/deplot')
+processor = Pix2StructProcessor.from_pretrained('google/deplot')
+url = "https://raw.githubusercontent.com/vis-nlp/ChartQA/main/ChartQA%20Dataset/val/png/5090.png"
+image = Image.open(requests.get(url, stream=True).raw)
+
+inputs = processor(images=image, text="Generate underlying data table of the figure below:", return_tensors="pt")
+predictions = model.generate(**inputs, max_new_tokens=512)
+print(processor.decode(predictions[0], skip_special_tokens=True))
+```
+
+# Contribution
+
+This model was originally contributed by Fangyu Liu, Julian Martin Eisenschlos et al. and added to the Hugging Face ecosystem by [Younes Belkada](https://huggingface.co/ybelkada).
+
+# Citation
+
+If you want to cite this work, please consider citing the original paper:
+```
+@misc{liu2022matcha,
+      title={MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering}, 
+      author={Fangyu Liu and Francesco Piccinno and Syrine Krichene and Chenxi Pang and Kenton Lee and Mandar Joshi and Yasemin Altun and Nigel Collier and Julian Martin Eisenschlos},
+      year={2022},
+      eprint={2212.09662},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```