Update README.md

README.md

@@ -12,9 +12,14 @@ pipeline_tag: visual-question-answering
 
 ## Model Description
 
-**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.
+**paligemma-3b-tablevqa-896-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.
+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
 
@@ -27,7 +32,9 @@ This model is designed for multilingual environments (French and English) and ex
 
 ## Model Architecture
 
-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.
+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
 
@@ -40,7 +47,7 @@ import requests
 import torch
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
-model_id = "cmarkea/paligemma-table-vqa-lora"
+model_id = "cmarkea/paligemma-tablevqa-896-lora"
 
 # 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"
@@ -71,15 +78,18 @@ with torch.inference_mode():
 
 ## Performance
 
-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.
+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.
 
-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.
+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.
 
 Here’s a visualization of the results:
 
 ![constellation](https://i.postimg.cc/y6tkYg9F/constellation-03.png)
 
-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.
+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.
 
 
 ## Citation
@@ -87,7 +97,7 @@ In comparison, this model outperforms **[HuggingFaceM4/Idefics3-8B-Llama3](https
 ```bibtex
 @online{AgDePaligemmaTabVQA,
   AUTHOR = {Tom Agonnoude, Cyrile Delestre},
-  URL = {https://huggingface.co/cmarkea/paligemma-table-vqa-lora},
+  URL = {https://huggingface.co/cmarkea/paligemma-tablevqa-896-lora},
   YEAR = {2024},
   KEYWORDS = {Multimodal, VQA, Table Understanding, LoRA},
 }