Update README.md

README.md

@@ -1,202 +1,93 @@
 ---
-base_model: google/paligemma-3b-ft-docvqa-896
-library_name: peft
+base_model: google/paligemma-3b-ft-docvqa-896 
+library_name: peft 
+license: apache-2.0 
+datasets: 
+- cmarkea/table-vqa 
+language: 
+- fr 
+- en 
+pipeline_tag: visual-question-answering 
 ---
 
-# Model Card for Model ID
+## Model Description
 
-<!-- Provide a quick summary of what the model is/does. -->
+**paligemma-3b-table-vqa-lora** is a fine-tuned version of the **[google/paligemma-3b-ft-docvqa-896](https://huggingface.co/google/paligemma-3b-ft-docvqa-896)** model, trained specifically on the **[table-vqa](https://huggingface.co/datasets/cmarkea/table-vqa)** dataset published by Crédit Mutuel Arkéa. This model leverages the **LoRA** (Low-Rank Adaptation) technique, which significantly reduces the computational complexity of fine-tuning while maintaining high performance. The model operates in bfloat16 precision for efficiency, making it an ideal solution for resource-constrained environments.
 
+This model is designed for multilingual environments (French and English) and excels in table-based visual question-answering (VQA) tasks. It is highly suitable for extracting information from tables in documents, making it a strong candidate for applications in financial reporting, data analysis, or administrative document processing. The model was fine-tuned over a span of 7 days using a single A100 40GB GPU.
 
+## Key Features
 
-## Model Details
+- **Language:** Multilingual capabilities, optimized for French and English.
+- **Model Type:** Multi-modal (image-text-to-text).
+- **Precision:** bfloat16 for resource efficiency.
+- **Training Duration:** 7 days on A100 40GB GPU.
+- **Fine-Tuning Method:** LoRA (Low-Rank Adaptation).
+- **Domain:** Table-based visual question answering.
 
-### Model Description
+## Model Architecture
 
-<!-- Provide a longer summary of what this model is. -->
+This model was built on top of **[google/paligemma-3b-ft-docvqa-896](https://huggingface.co/google/paligemma-3b-ft-docvqa-896)**, using its pre-trained multi-modal capabilities to process both text and images (e.g., document tables). LoRA was applied to reduce the size and complexity of fine-tuning while preserving accuracy, allowing the model to excel in specific tasks such as table understanding and VQA.
 
+## Usage
 
+You can use this model for visual question answering with table-based data by following the steps below:
 
-- **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]
+```python
+from transformers import AutoProcessor, PaliGemmaForConditionalGeneration
+from PIL import Image
+import requests
+import torch
+device = "cuda" if torch.cuda.is_available() else "cpu"
 
-### Model Sources [optional]
+model_id = "cmarkea/paligemma-table-vqa-lora"
 
-<!-- Provide the basic links for the model. -->
+# Sample image for inference
+url = "https://datasets-server.huggingface.co/cached-assets/cmarkea/table-vqa/--/c26968da3346f92ab6bfc5fec85592f8250e23f5/--/default/train/22/image/image.jpg?Expires=1728915081&Signature=Zkrd9ZWt5b9XtY0UFrgfrTuqo58DHWIJ00ZwXAymmL-mrwqnWWmiwUPelYOOjPZZdlP7gAvt96M1PKeg9a2TFm7hDrnnRAEO~W89li~AKU2apA81M6AZgwMCxc2A0xBe6rnCPQumiCGD7IsFnFVwcxkgMQXyNEL7bEem6cT0Cief9DkURUDCC-kheQY1hhkiqLLUt3ITs6o2KwPdW97EAQ0~VBK1cERgABKXnzPfAImnvjw7L-5ZXCcMJLrvuxwgOQ~DYPs456ZVxQLbTxuDwlxvNbpSKoqoAQv0CskuQwTFCq2b5MOkCCp9zoqYJxhUhJ-aI3lhyIAjmnsL4bhe6A__&Key-Pair-Id=K3EI6M078Z3AC3"
+image = Image.open(requests.get(url, stream=True).raw)
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+# Load the fine-tuned model and processor
+model = PaliGemmaForConditionalGeneration.from_pretrained(
+ model_id,
+ torch_dtype=torch.bfloat16,
+ device_map=device,
+).eval()
 
-## Uses
+processor = AutoProcessor.from_pretrained("google/paligemma-3b-ft-docvqa-896")
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+# Input prompt for table VQA
+prompt = "How many rows are in this table?"
+model_inputs = processor(text=prompt, images=image, return_tensors="pt").to(model.device)
 
-### Direct Use
+# Generate the answer
+input_len = model_inputs["input_ids"].shape[-1]
+with torch.inference_mode():
+ generation = model.generate(**model_inputs, max_new_tokens=100, do_sample=False)
+ generation = generation[0][input_len:]
+ decoded = processor.decode(generation, skip_special_tokens=True)
+ print(decoded)
+```
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-[More Information Needed]
+## Performance
 
-### Downstream Use [optional]
+The model's performance was evaluated on 200 question-answer pairs, extracted from 100 tables from the test set of the **[table-vqa](https://huggingface.co/datasets/cmarkea/table-vqa)** dataset. For each table, two pairs were selected: one in French and the other in English.
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+To evaluate the model’s responses, the **[LLM-as-Juries](https://arxiv.org/abs/2404.18796)** framework was employed using three judge models (GPT-4o, Gemini1.5 Pro, and Claude 3.5-Sonnet). The evaluation was based on a scale from 0 to 5, tailored to the VQA context, ensuring accurate judgment of the model’s performance.
 
-[More Information Needed]
+Here’s a visualization of the results:
 
-### Out-of-Scope Use
+![constellation](https://i.postimg.cc/t4tjhy6b/constellation-0.png)
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+In comparison, this model outperforms **[HuggingFaceM4/Idefics3-8B-Llama3](https://huggingface.co/HuggingFaceM4/Idefics3-8B-Llama3)** in terms of accuracy and efficiency, despite having a smaller parameter size.
 
-[More Information Needed]
 
-## Bias, Risks, and Limitations
+## Citation
 
-<!-- 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.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## 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. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[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]
-### Framework versions
-
-- PEFT 0.12.0
+```bibtex
+@online{AgDePaligemmaTabVQA,
+ AUTHOR = {Tom Agonnoude, Cyrile Delestre},
+ URL = {https://huggingface.co/cmarkea/paligemma-table-vqa-lora},
+ YEAR = {2024},
+ KEYWORDS = {Multimodal, VQA, Table Understanding, LoRA},
+}