Update README.md

README.md

@@ -20,31 +20,16 @@ pipeline_tag: visual-question-answering
 
 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]
+- **Developed by:** Aniket Maurya
+- **Model type:** Visual language model
+- **License:** MIT
+- **Finetuned from model [optional]:** microsoft/Florence-2-base-ft
 
-### 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. -->
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+Use this model for extracting total amount from a receipt.
 
-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
 
@@ -60,10 +45,10 @@ from transformers import AutoProcessor, AutoModelForCausalLM
 device = "cuda:0" if torch.cuda.is_available() else "cpu"
 torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
 
-model = AutoModelForCausalLM.from_pretrained("microsoft/Florence-2-base-ft", torch_dtype=torch_dtype, trust_remote_code=True).to(device)
+model = AutoModelForCausalLM.from_pretrained("aniketmaurya/receipt-model-2025", torch_dtype=torch_dtype, trust_remote_code=True).to(device)
 processor = AutoProcessor.from_pretrained("microsoft/Florence-2-base-ft", trust_remote_code=True)
 
-prompt = "<OD>"
+prompt = "<VQA>Given the following receipt, extract the total amount spent."
 
 url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true"
 image = Image.open(requests.get(url, stream=True).raw)
@@ -73,15 +58,13 @@ inputs = processor(text=prompt, images=image, return_tensors="pt").to(device, to
 generated_ids = model.generate(
     input_ids=inputs["input_ids"],
     pixel_values=inputs["pixel_values"],
-    max_new_tokens=1024,
+    max_new_tokens=100,
     do_sample=False,
     num_beams=3
 )
 generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
 
-parsed_answer = processor.post_process_generation(generated_text, task="<OD>", image_size=(image.width, image.height))
-
-print(parsed_answer)
+print(generated_text)
 ```
 
 
@@ -89,123 +72,40 @@ print(parsed_answer)
 
 ### 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. -->
+Public receipt data comprising 216 images forked from Roboflow universe and annotated manually for total amount.
 
-[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. -->
+Training configuration
+```
+{
+    'model_id': 'microsoft/Florence-2-base-ft',
+    'revision': 'refs/pr/20',
+    'epochs': 30,
+    'optimizer': 'adamw',
+    'lr': 5e-06,
+    'lr_scheduler': 'linear',
+    'batch_size': 8,
+    'val_batch_size': None,
+    'num_workers': 0,
+    'val_num_workers': None,
+    'lora_r': 8,
+    'lora_alpha': 8,
+    'lora_dropout': 0.05,
+    'bias': 'none',
+    'use_rslora': True,
+    'init_lora_weights': 'gaussian',
+}
+```
 
 #### Preprocessing [optional]
 
-[More Information Needed]
-
+**Image augmentations:**
 
-#### Training Hyperparameters
+- shear, random rotate, and noise
 
-- **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]
+Vibe check 😎