Update README.md

README.md

@@ -1,199 +1,97 @@
 ---
 library_name: transformers
-tags: []
+license: apache-2.0
+datasets:
+- alakxender/dhivehi-image-text
+language:
+- dv
+base_model:
+- facebook/deit-base-distilled-patch16-384
 ---
+# TrOCR Finetuned for Dhivehi Text Recognition
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+A TrOCR model finetuned for Dhivehi (Divehi/Maldivian) text recognition using DeiT base encoder and BERT decoder.
 
 ## Model Details
 
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-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]
-- **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]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[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 -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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-[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. -->
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-[More Information Needed]
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-### Training Procedure
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-<!-- 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. -->
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-[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. -->
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-[More Information Needed]
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-[More Information Needed]
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-#### Metrics
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-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-[More Information Needed]
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-### Results
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-[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
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-[More Information Needed]
-
-#### Hardware
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-[More Information Needed]
-
-#### Software
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-[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:**
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-[More Information Needed]
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-**APA:**
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-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-[More Information Needed]
-
-## More Information [optional]
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-
-## Model Card Authors [optional]
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-[More Information Needed]
-
-## Model Card Contact
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-[More Information Needed]
+- Base models:
+  - Encoder: facebook/deit-base-distilled-patch16-384
+  - Decoder: alakxender/bert-base-dv
+- Training data: 10k samples with 90/10 train/test split
+- Input size: 384x384 pixels
+- Beam search parameters:
+  - max_length: 64
+  - num_beams: 4
+  - early_stopping: True
+  - length_penalty: 2.0
+  - no_repeat_ngram_size: 3
+
+## Training
+
+The model was trained with:
+- 7 epochs
+- Batch size: 8
+- Learning rate: 4e-5
+- FP16 mixed precision
+- Training augmentations:
+  - Elastic transform (α=8.0, σ=5.0)
+  - Gaussian blur (kernel size=(5,9), σ=(0.1,5))
+  - Resize (384x384)
+  - Normalization ([0.5,0.5,0.5], [0.5,0.5,0.5])
+
+## Usage
+
+```python
+from PIL import Image
+import torch
+from transformers import DeiTImageProcessor, TrOCRProcessor, VisionEncoderDecoderModel, AutoTokenizer
+
+def load_model():
+    # Initialize components
+    tokenizer = AutoTokenizer.from_pretrained("alakxender/bert-base-dv")
+    image_processor = DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-384")
+    processor = TrOCRProcessor(image_processor=image_processor, tokenizer=tokenizer)
+    model = VisionEncoderDecoderModel.from_pretrained("alakxender/trocr-dv-diet-base-bert")
+    
+    # Set device
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    model = model.to(device)
+    
+    return model, processor, device
+
+def predict(image_path, model, processor, device):
+    # Load and process image
+    image = Image.open(image_path).convert("RGB")
+    pixel_values = processor(image, return_tensors="pt").pixel_values.to(device)
+    
+    # Generate prediction
+    outputs = model.generate(
+        pixel_values,
+        max_length=64,
+        num_beams=4,
+        early_stopping=True,
+        length_penalty=2.0,
+        no_repeat_ngram_size=3
+    )
+    
+    # Decode prediction
+    predicted_text = processor.decode(outputs[0], skip_special_tokens=True)
+    return predicted_text
+
+# Example usage
+model, processor, device = load_model()
+predicted_text = predict("example.jpg", model, processor, device)
+print(predicted_text)
+```
+
+## Evaluation Results
+
+```json
+[
+  {
+    "file_name": "data/images/DV01-04/DV01-04_140.jpg",
+    "predicted_text": "ޤާނޫނުގެ 42 ވަނަ މާއްދާގައި ލާޒިމްކުރާ މި ރިޕޯޓު ތައްޔާރުކޮށް ފޮނުވުމުގެ ޒިންމާއަކީ ޤާނޫނުން އިދާރާގެ އިންފޮމޭޝަން އޮފިސަރު ކުރައްވަންޖެހޭ ކަމެކެވެ .",
+    "true_text": "ޤާނޫނުގެ 42 ވަނަ މާއްދާގައި ލާޒިމްކުރާ މި ރިޕޯޓު ތައްޔާރުކޮށް ފޮނުވުމުގެ ޒިންމާއަކީ ޤާނޫނުން އިދާރާގެ އިންފޮމޭޝަން އޮފިސަރު ކުރައްވަންޖެހޭ ކަމެކެވެ."
+  }
+]
+```