Update README.mt

README.md

@@ -1,6 +1,7 @@
 ---
 library_name: transformers
-tags: []
+language:
+- ur
 ---
 
 # Model Card for Model ID
@@ -8,6 +9,9 @@ tags: []
 <!-- Provide a quick summary of what the model is/does. -->
 
 
+This is an Urdu OCR model designed for handwriting recognition tasks. It utilizes a VisionEncoderDecoderModel with a ViT-based encoder and a BERT-based decoder, fine-tuned on a custom dataset for robust and accurate text extraction from images.
+
+
 
 ## Model Details
 
@@ -17,13 +21,13 @@ tags: []
 
 This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
 
-- **Developed by:** [More Information Needed]
+- **Developed by:** Fajar Pervaiz
 - **Funded by [optional]:** [More Information Needed]
 - **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
+- **Model type:** VisionEncoderDecoderModel
+- **Language(s) (NLP):** Urdu (ur)
 - **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Finetuned from model [optional]:** facebook/deit-base-distilled-patch16-384, bert-base-multilingual-cased
 
 ### Model Sources [optional]
 
@@ -40,24 +44,28 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 ### Direct Use
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+This model can be directly used for Urdu handwriting recognition tasks, particularly for extracting text from scanned documents or handwritten notes.
 
 [More Information Needed]
 
 ### Downstream Use [optional]
 
 <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+This model can be fine-tuned further for specific handwriting datasets or integrated into larger OCR systems for Urdu or multilingual text recognition.
 
 [More Information Needed]
 
 ### Out-of-Scope Use
 
 <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+The model is not suitable for languages other than Urdu or domains with highly noisy or distorted images without further fine-tuning.
 
 [More Information Needed]
 
 ## Bias, Risks, and Limitations
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
+The model may exhibit biases inherent in the training data. Misrecognition of complex or ambiguous handwriting is possible. Users should carefully evaluate its performance in their specific use case.
 
 [More Information Needed]
 
@@ -65,25 +73,32 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 <!-- 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.
-
+Users should test the model thoroughly on their specific dataset and consider additional fine-tuning if required. Misuse in sensitive applications (e.g., legal or medical document OCR) should be avoided without rigorous evaluation.
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.
 
+from transformers import VisionEncoderDecoderModel, TrOCRProcessor
+processor = TrOCRProcessor.from_pretrained("path/to/processor")
+model = VisionEncoderDecoderModel.from_pretrained("path/to/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. -->
+The training data comprises 46,742 image-text pairs from a custom dataset of Urdu handwritten texts.
 
 [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. -->
+Images were resized to 384x384 pixels and normalized. Augmentations such as Elastic Transform and Gaussian Blur were applied to enhance robustness.
 
 #### Preprocessing [optional]
 
@@ -93,6 +108,12 @@ Use the code below to get started with the model.
 #### 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 -->
+- Training regime: Mixed precision (fp16)
+- Learning rate: 4e-5
+- Batch size: 8
+- Epochs: 12
+- Optimizer: AdamW
+- Scheduler: Linear decay
 
 #### Speeds, Sizes, Times [optional]
 
@@ -109,12 +130,16 @@ Use the code below to get started with the model.
 #### Testing Data
 
 <!-- This should link to a Dataset Card if possible. -->
+A subset of 4,675 image-text pairs was used for evaluation.
 
 [More Information Needed]
 
 #### Factors
 
 <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+The model was tested on handwritten text images with varying font styles and complexities.
+
+
 
 [More Information Needed]
 
@@ -144,7 +169,7 @@ Use the code below to get started with the model.
 
 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]
+- **Hardware Type:** NVIDIA GPU
 - **Hours used:** [More Information Needed]
 - **Cloud Provider:** [More Information Needed]
 - **Compute Region:** [More Information Needed]
@@ -154,7 +179,8 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+The model uses a VisionEncoderDecoder architecture combining a ViT encoder and a BERT decoder.
+
 
 ### Compute Infrastructure
 
@@ -162,11 +188,15 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 #### Hardware
 
-[More Information Needed]
+NVIDIA GPU (e.g., A100)
+
 
 #### Software
 
 [More Information Needed]
+Python, PyTorch, Hugging Face Transformers
+
+
 
 ## Citation [optional]
 
@@ -183,6 +213,9 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 ## Glossary [optional]
 
 <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+CER: Character Error Rate
+WER: Word Error Rate
+OCR: Optical Character Recognition
 
 [More Information Needed]
 
@@ -193,7 +226,9 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 ## Model Card Authors [optional]
 
 [More Information Needed]
+Fajar Pervaiz
 
 ## Model Card Contact
 
-[More Information Needed]
+[More Information Needed]
+[email protected]