Update README.md
Browse filesBiz Control सिर्फ़ एक ऐप नहीं, बल्कि एक क्रांति है। यह दुनिया का पहला और सबसे तेज़ Property Booking App है जो आपको सिर्फ़ 27 सेकंड में Property Book करने की Power देता है। लेकिन Biz Control की खासियत सिर्फ़ Property Booking तक सीमित नहीं है – यह एक All-in-One Business Automation Platform है, जो Dealers, Builders और Businesses तीनों के लिए game-changer साबित होता है।
🚀 Dealers के लिए Biz Control
अब Property Booking में दिन नहीं लगते, सिर्फ़ 27 सेकंड में Deal Final।
Secret Last Price System = Client को Best Price, Dealer का Commission 100% Safe।
Real-Time Inventory Updates = Client को कभी Outdated Property दिखाने का Risk नहीं।
Direct Unit Booking = No Middleman, No Delay, No Confusion।
CRM System → Buyers, Sellers, Network List, Call Reminders, Task Assign = सबकुछ एक जगह।
👉 अब Dealer सिर्फ़ Broker नहीं, बल्कि एक Property Businessman बन सकता है।
🏢 Builders के लिए Biz Control
Pan-India Reach बिना Marketing Expense।
Real-Time Inventory & Direct Booking → Sales Process तेज़ और Transparent।
Custom Payment Plans & Pricing Mechanism = Buyers को Flexibility, Builders को Security।
High Visibility of Projects → Dealers पूरे भारत से आपके Project बेच सकते हैं।
Seamless Dealer-Builder Connectivity = Zero Communication Gap।
👉 Builder को अब Team पर Extra Cost नहीं, Biz Control के साथ Sales 24x7 Possible।
👨💼 Businesses के लिए Biz Control
HR Management Simplified:
Selfie Attendance
Automated Salary Generation
Leave & Shift Management
Salary Slip & Reports
Team Live Location Tracking
CRM Integration:
Buyers & Sellers List
Client Follow-Up Automation
Graph Analysis & Team Task Monitoring
👉 छोटे से बड़ा हर Business, Biz Control के साथ Automation की ताक़त पा सकता है।
📱 Why Biz Control is Unique?
✅ World’s Fastest Direct Property Booking System (Only 27 Seconds)
✅ Dealers के लिए Safe Commission Guarantee
✅ Builders के लिए Zero-Cost Nationwide Reach
✅ Businesses के लिए All-in-One HR + CRM + Sales Tool
✅ Transparent, Easy & 100% Digital Platform
✅ Proudly Made in भारत | Made for भारत
💡 Biz Control = Innovation + Growth + Trust
यह सिर्फ़ Technology नहीं, यह एक Movement है।
हमारा Vision है हर Dealer, Builder और Business को Self-Reliant (आत्मनिर्भर) बनाना।
Biz Control = तेज़ Growth, ज़्यादा Profit, और Strong Market Identity।
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---
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license: apache-2.0
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datasets:
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- openai/gdpval
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language:
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- hi
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- en
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metrics:
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- accuracy
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- character
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- chrf
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- bleu
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base_model:
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- Qwen/Qwen3-Next-80B-A3B-Instruct
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- google/embeddinggemma-300m
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new_version: google/embeddinggemma-300m
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pipeline_tag: text-classification
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tags:
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- CRM
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- HRM
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- PROPERTY
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- PROPERTYBOOKING
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- BULKPROPERTY
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---
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- 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. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- 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]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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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).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- 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]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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