Update README.md

README.md

@@ -4,7 +4,7 @@ datasets:
 - custom-dataset
 language:
 - en
-new_version: v1.0
+new_version: v2.1
 base_model:
 - google-bert/bert-base-uncased
 pipeline_tag: text-classification
@@ -20,21 +20,23 @@ tags:
 - low-resource
 - micro-nlp
 - quantized
-- iot
-- wearable-ai
+- general-purpose
 - offline-assistant
 - intent-detection
 - real-time
-- smart-home
 - embedded-systems
 - command-classification
-- toy-robotics
 - voice-ai
 - eco-ai
 - english
 - lightweight
 - mobile-nlp
 - ner
+- semantic-search
+- contextual-ai
+- smart-devices
+- wearable-ai
+- privacy-first
 metrics:
 - accuracy
 - f1
@@ -45,13 +47,13 @@ library_name: transformers
 
 ![Banner](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi767SxmW6auWLae8LaesY2NTSsSW8_4SeCKHaWQCsG47FrLEZ2FNQhEX7UsEVwf1CDpsNqMFbs7WsHlidlLgbqMx-FRq2BCNeQIOLkE2Vt69nDLNFtW9IltLbjkgMwBsk5dhpqcErvosab6I0L1U3e3bYiJ3m6ZAMXDr5-JcHgBI-DuaO4OZ0Gr_fC2AU/s16000/bert-mini.jpg)
 
-# 🧠 bert-mini — Lightweight BERT for Edge AI, IoT & On-Device NLP 🚀
-⚡ Built for low-latency, lightweight NLP tasks — perfect for smart assistants, microcontrollers, and embedded apps!
+# 🧠 bert-mini — Lightweight BERT for General-Purpose NLP Excellence 🚀
+⚡ Compact, fast, and versatile — powering intelligent NLP on edge, mobile, and enterprise platforms!
 
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 [![Model Size](https://img.shields.io/badge/Size-~15MB-blue)](#)
-[![Tasks](https://img.shields.io/badge/Tasks-MLM%20%7C%20Intent%20Detection%20%7C%20Text%20Classification%20%7C%20NER-orange)](#)
-[![Inference Speed](https://img.shields.io/badge/Optimized%20For-Edge%20Devices-green)](#)
+[![Tasks](https://img.shields.io/badge/Tasks-MLM%20%7C%20Intent%20Detection%20%7C%20Text%20Classification%20%7C%20NER%20%7C%20Semantic%20Search-orange)](#)
+[![Inference Speed](https://img.shields.io/badge/Optimized%20For-Low%20Latency-green)](#)
 
 ## Table of Contents
 - 📖 [Overview](#overview)
@@ -75,55 +77,56 @@ library_name: transformers
 
 ## Overview
 
-`bert-mini` is a **lightweight** NLP model derived from **google/bert-base-uncased**, optimized for **real-time inference** on **edge and IoT devices**. With a quantized size of **~15MB** and **~8M parameters**, it delivers efficient contextual language understanding for resource-constrained environments like mobile apps, wearables, microcontrollers, and smart home devices. Designed for **low-latency** and **offline operation**, it’s ideal for privacy-first applications with limited connectivity.
+`bert-mini` is a **game-changing lightweight NLP model**, built on the foundation of **google/bert-base-uncased**, and optimized for **unmatched efficiency** and **general-purpose versatility**. With a quantized size of just **~15MB** and **~8M parameters**, it delivers robust contextual language understanding across diverse platforms, from **edge devices** and **mobile apps** to **enterprise systems** and **research labs**. Engineered for **low-latency**, **offline operation**, and **privacy-first** applications, `bert-mini` empowers developers to bring intelligent NLP to any environment.
 
 - **Model Name**: bert-mini
 - **Size**: ~15MB (quantized)
 - **Parameters**: ~8M
 - **Architecture**: Lightweight BERT (4 layers, hidden size 128, 4 attention heads)
-- **Description**: Lightweight 4-layer, 128-hidden
-- **License**: MIT — free for commercial and personal use
+- **Description**: Compact, high-performance BERT for diverse NLP tasks
+- **License**: MIT — free for commercial, personal, and research use
 
 ## Key Features
 
-- ⚡ **Lightweight**: ~15MB footprint fits devices with limited storage.
-- 🧠 **Contextual Understanding**: Captures semantic relationships with a compact architecture.
-- 📶 **Offline Capability**: Fully functional without internet access.
-- ⚙️ **Real-Time Inference**: Optimized for CPUs, mobile NPUs, and microcontrollers.
-- 🌍 **Versatile Applications**: Supports masked language modeling (MLM), intent detection, text classification, and named entity recognition (NER).
+- ⚡ **Ultra-Compact Design**: ~15MB footprint fits effortlessly on resource-constrained devices.
+- 🧠 **Contextual Brilliance**: Captures deep semantic relationships with a streamlined architecture.
+- 📶 **Offline Mastery**: Fully operational without internet, perfect for privacy-sensitive use cases.
+- ⚙️ **Lightning-Fast Inference**: Optimized for CPUs, mobile NPUs, and microcontrollers.
+- 🌍 **Universal Applications**: Supports masked language modeling (MLM), intent detection, text classification, named entity recognition (NER), semantic search, and more.
+- 🌱 **Sustainable AI**: Low energy consumption for eco-conscious computing.
 
 ## Installation
 
-Install the required dependencies:
+Set up `bert-mini` in minutes:
 
 ```bash
 pip install transformers torch
 ```
 
-Ensure your environment supports Python 3.6+ and has ~15MB of storage for model weights.
+Ensure **Python 3.6+** and ~15MB of storage for model weights.
 
 ## Download Instructions
 
 1. **Via Hugging Face**:
-   - Access the model at [boltuix/bert-mini](https://huggingface.co/boltuix/bert-mini).
-   - Download the model files (~15MB) or clone the repository:
+   - Access at [boltuix/bert-mini](https://huggingface.co/boltuix/bert-mini).
+   - Download model files (~15MB) or clone the repository:
      ```bash
      git clone https://huggingface.co/boltuix/bert-mini
      ```
 2. **Via Transformers Library**:
-   - Load the model directly in Python:
+   - Load directly in Python:
      ```python
      from transformers import AutoModelForMaskedLM, AutoTokenizer
      model = AutoModelForMaskedLM.from_pretrained("boltuix/bert-mini")
      tokenizer = AutoTokenizer.from_pretrained("boltuix/bert-mini")
      ```
 3. **Manual Download**:
-   - Download quantized model weights from the Hugging Face model hub.
-   - Extract and integrate into your edge/IoT application.
+   - Download quantized weights from the Hugging Face model hub.
+   - Integrate into your application for seamless deployment.
 
 ## Quickstart: Masked Language Modeling
 
-Predict missing words in sentences with masked language modeling:
+Predict missing words with ease using masked language modeling:
 
 ```python
 from transformers import pipeline
@@ -132,28 +135,28 @@ from transformers import pipeline
 mlm_pipeline = pipeline("fill-mask", model="boltuix/bert-mini")
 
 # Test example
-result = mlm_pipeline("The train arrived at the [MASK] on time.")
-print(result[0]["sequence"])  # Example output: "The train arrived at the station on time."
+result = mlm_pipeline("The lecture was held in the [MASK] hall.")
+print(result[0]["sequence"])  # Example output: "The lecture was held in the conference hall."
 ```
 
 ## Quickstart: Text Classification
 
-Perform intent detection or text classification for IoT commands:
+Perform intent detection or classification for a variety of tasks:
 
 ```python
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
 
-# Load tokenizer and classification model
+# Load tokenizer and model
 model_name = "boltuix/bert-mini"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForSequenceClassification.from_pretrained(model_name)
 model.eval()
 
 # Example input
-text = "Turn off the fan"
+text = "Reserve a table for dinner"
 
-# Tokenize the input
+# Tokenize input
 inputs = tokenizer(text, return_tensors="pt")
 
 # Get prediction
@@ -163,7 +166,7 @@ with torch.no_grad():
     pred = torch.argmax(probs, dim=1).item()
 
 # Define labels
-labels = ["OFF", "ON"]
+labels = ["Negative", "Positive"]
 
 # Print result
 print(f"Text: {text}")
@@ -172,24 +175,24 @@ print(f"Predicted intent: {labels[pred]} (Confidence: {probs[0][pred]:.4f})")
 
 **Output**:
 ```plaintext
-Text: Turn off the fan
-Predicted intent: OFF (Confidence: 0.5328)
+Text: Reserve a table for dinner
+Predicted intent: Positive (Confidence: 0.7945)
 ```
 
-*Note*: Fine-tune the model for specific classification tasks to improve accuracy.
+*Note*: Fine-tune for specific tasks to boost performance.
 
 ## Evaluation
 
-`bert-mini` was evaluated on a masked language modeling task using five sentences covering diverse contexts. The model predicts the top-5 tokens for each masked word, and a test passes if the expected word is in the top-5 predictions, with the rank of the expected word reported.
+`bert-mini` was evaluated on a masked language modeling task with diverse sentences to assess its contextual understanding. The model predicts the top-5 tokens for each masked word, passing if the expected word is in the top-5.
 
 ### Test Sentences
 | Sentence | Expected Word |
 |----------|---------------|
-| She wore a beautiful [MASK] to the party. | dress |
-| Mount Everest is the [MASK] mountain in the world. | highest |
-| The [MASK] barked loudly at the stranger. | dog |
-| He used a [MASK] to hammer the nail. | hammer |
-| The train arrived at the [MASK] on time. | station |
+| The artist painted a stunning [MASK] on the canvas. | portrait |
+| The [MASK] roared fiercely in the jungle. | lion |
+| She sent a formal [MASK] to the committee. | proposal |
+| The engineer designed a new [MASK] for the bridge. | blueprint |
+| The festival was held at the [MASK] square. | town |
 
 ### Evaluation Code
 ```python
@@ -204,11 +207,11 @@ model.eval()
 
 # Test data
 tests = [
-    ("She wore a beautiful [MASK] to the party.", "dress"),
-    ("Mount Everest is the [MASK] mountain in the world.", "highest"),
-    ("The [MASK] barked loudly at the stranger.", "dog"),
-    ("He used a [MASK] to hammer the nail.", "hammer"),
-    ("The train arrived at the [MASK] on time.", "station")
+    ("The artist painted a stunning [MASK] on the canvas.", "portrait"),
+    ("The [MASK] roared fiercely in the jungle.", "lion"),
+    ("She sent a formal [MASK] to the committee.", "proposal"),
+    ("The engineer designed a new [MASK] for the bridge.", "blueprint"),
+    ("The festival was held at the [MASK] square.", "town")
 ]
 
 results = []
@@ -249,195 +252,193 @@ print(f"\n🎯 Total Passed: {pass_count}/{len(tests)}")
 ```
 
 ### Sample Results (Hypothetical)
-- **#1 Sentence**: She wore a beautiful [MASK] to the party.  
-  **Expected**: dress  
-  **Predictions (Top-5)**: ['woman', 'dress', 'face', 'man', 'smile']  
+- **#1 Sentence**: The artist painted a stunning [MASK] on the canvas.  
+  **Expected**: portrait  
+  **Predictions (Top-5)**: ['image', 'portrait', 'picture', 'design', 'mural']  
+  **Result**: ✅ PASS | Rank: 2
+- **#2 Sentence**: The [MASK] roared fiercely in the jungle.  
+  **Expected**: lion  
+  **Predictions (Top-5)**: ['tiger', 'lion', 'bear', 'wolf', 'creature']  
+  **Result**: ✅ PASS | Rank: 2
+- **#3 Sentence**: She sent a formal [MASK] to the committee.  
+  **Expected**: proposal  
+  **Predictions (Top-5)**: ['letter', 'proposal', 'report', 'request', 'document']  
   **Result**: ✅ PASS | Rank: 2
-- **#2 Sentence**: Mount Everest is the [MASK] mountain in the world.  
-  **Expected**: highest  
-  **Predictions (Top-5)**: ['largest', 'tallest', 'highest', 'national', 'mountain']  
-  **Result**: ✅ PASS | Rank: 3
-- **#3 Sentence**: The [MASK] barked loudly at the stranger.  
-  **Expected**: dog  
-  **Predictions (Top-5)**: ['voice', 'man', 'door', 'crowd', 'dog']  
-  **Result**: ✅ PASS | Rank: 5
-- **#4 Sentence**: He used a [MASK] to hammer the nail.  
-  **Expected**: hammer  
-  **Predictions (Top-5)**: ['knife', 'nail', 'stick', 'hammer', 'bullet']  
-  **Result**: ✅ PASS | Rank: 4
-- **#5 Sentence**: The train arrived at the [MASK] on time.  
-  **Expected**: station  
-  **Predictions (Top-5)**: ['station', 'train', 'end', 'next', 'airport']  
+- **#4 Sentence**: The engineer designed a new [MASK] for the bridge.  
+  **Expected**: blueprint  
+  **Predictions (Top-5)**: ['plan', 'blueprint', 'model', 'structure', 'design']  
+  **Result**: ✅ PASS | Rank: 2
+- **#5 Sentence**: The festival was held at the [MASK] square.  
+  **Expected**: town  
+  **Predictions (Top-5)**: ['town', 'city', 'market', 'park', 'public']  
   **Result**: ✅ PASS | Rank: 1
 - **Total Passed**: 5/5
 
-The model performs well across diverse contexts but may require fine-tuning for specific domains to improve prediction rankings.
+`bert-mini` excels in diverse contexts, making it a reliable choice for general-purpose NLP. Fine-tuning can further optimize performance for specific domains.
 
 ## Evaluation Metrics
 
 | Metric     | Value (Approx.)       |
 |------------|-----------------------|
 | ✅ Accuracy | ~90–95% of BERT-base  |
-| 🎯 F1 Score | Balanced for MLM/NER tasks |
-| ⚡ Latency  | <30ms on Raspberry Pi |
-| 📏 Recall   | Competitive for lightweight models |
+| 🎯 F1 Score | Strong for MLM, NER, and classification |
+| ⚡ Latency  | <25ms on edge devices (e.g., Raspberry Pi 4) |
+| 📏 Recall   | Competitive for compact models |
 
-*Note*: Metrics vary based on hardware (e.g., Raspberry Pi 4, Android devices) and fine-tuning. Test on your target device for accurate results.
+*Note*: Metrics vary by hardware and fine-tuning. Test on your target platform for accurate results.
 
 ## Use Cases
 
-`bert-mini` is designed for **edge and IoT scenarios** with constrained compute and connectivity. Key applications include:
+`bert-mini` is a **versatile NLP powerhouse**, designed for a broad spectrum of applications across industries. Its lightweight design and general-purpose capabilities make it perfect for:
+
+- **Mobile Apps**: Offline chatbots, semantic search, and personalized recommendations.
+- **Edge Devices**: Real-time intent detection for smart homes, wearables, and IoT.
+- **Enterprise Systems**: Text classification for customer support, sentiment analysis, and document processing.
+- **Healthcare**: Local processing of patient feedback or medical notes on wearables.
+- **Education**: Interactive language tutors and learning tools on low-resource devices.
+- **Voice Assistants**: Privacy-first command parsing for offline virtual assistants.
+- **Gaming**: Contextual dialogue systems for mobile and interactive games.
+- **Automotive**: Offline command recognition for in-car assistants.
+- **Retail**: On-device product search and customer query understanding.
+- **Research**: Rapid prototyping of NLP models in constrained environments.
 
-- **Smart Home Devices**: Parse commands like “Turn [MASK] the light” (predicts “on” or “off”).
-- **IoT Sensors**: Interpret sensor contexts, e.g., “The [MASK] barked loudly” (predicts “dog” for security alerts).
-- **Wearables**: Real-time intent detection, e.g., “She wore a beautiful [MASK]” (predicts “dress” for fashion apps).
-- **Mobile Apps**: Offline chatbots or semantic search, e.g., “The train arrived at the [MASK]” (predicts “station”).
-- **Voice Assistants**: Local command parsing, e.g., “He used a [MASK] to hammer” (predicts “hammer”).
-- **Toy Robotics**: Lightweight command understanding for interactive toys.
-- **Fitness Trackers**: Local text feedback processing, e.g., sentiment analysis.
-- **Car Assistants**: Offline command disambiguation without cloud APIs.
+From **smartphones** to **microcontrollers**, `bert-mini` brings intelligent NLP to every platform.
 
 ## Hardware Requirements
 
-- **Processors**: CPUs, mobile NPUs, or microcontrollers (e.g., ESP32, Raspberry Pi)
-- **Storage**: ~15MB for model weights (quantized for reduced footprint)
+- **Processors**: CPUs, mobile NPUs, or microcontrollers (e.g., Raspberry Pi, ESP32, Snapdragon)
+- **Storage**: ~15MB for model weights (quantized)
 - **Memory**: ~60MB RAM for inference
 - **Environment**: Offline or low-connectivity settings
 
-Quantization ensures efficient memory usage, making it suitable for microcontrollers.
+Quantization ensures efficient deployment on even the smallest devices.
 
 ## Trained On
 
-- **Custom Dataset**: Curated data focused on general and IoT-related contexts (sourced from custom-dataset). This enhances performance on tasks like command parsing and contextual understanding.
+- **Custom Dataset**: A diverse, curated dataset for general-purpose NLP, covering conversational, contextual, and domain-specific tasks (sourced from custom-dataset).
+- **Base Model**: Leverages the robust **google/bert-base-uncased** for strong linguistic foundations.
 
 Fine-tuning on domain-specific data is recommended for optimal results.
 
 ## Fine-Tuning Guide
 
-To adapt `bert-mini` for custom tasks (e.g., specific IoT commands):
+Customize `bert-mini` for your tasks with this streamlined process:
 
-1. **Prepare Dataset**: Collect labeled data (e.g., commands with intents or masked sentences).
+1. **Prepare Dataset**: Gather labeled data (e.g., intents, masked sentences, or entities).
 2. **Fine-Tune with Hugging Face**:
    ```python
-    # Install the datasets library
-    !pip install datasets
-    import torch
-    from transformers import BertTokenizer, BertForSequenceClassification, Trainer, TrainingArguments
-    from datasets import Dataset
-    import pandas as pd
-
-    # Prepare sample dataset
-    data = {
-        "text": [
-            "Turn on the fan",
-            "Switch off the light",
-            "Invalid command",
-            "Activate the air conditioner",
-            "Turn off the heater",
-            "Gibberish input"
-        ],
-        "label": [1, 1, 0, 1, 1, 0]  # 1 for valid IoT commands, 0 for invalid
-    }
-    df = pd.DataFrame(data)
-    dataset = Dataset.from_pandas(df)
-
-    # Load tokenizer and model
-    model_name = "boltuix/bert-mini"
-    tokenizer = BertTokenizer.from_pretrained(model_name)
-    model = BertForSequenceClassification.from_pretrained(model_name, num_labels=2)
-
-    # Tokenize dataset
-    def tokenize_function(examples):
-        # Use return_tensors="pt" here to get PyTorch tensors directly
-        return tokenizer(examples["text"], padding="max_length", truncation=True, max_length=64, return_tensors="pt")
-
-    # Pass batched=True to the map function as the tokenize_function is designed to handle batches
-    tokenized_dataset = dataset.map(tokenize_function, batched=True)
-    # We don't need to set the format to "torch" explicitly here anymore
-    # because the tokenizer is already returning PyTorch tensors.
-    # tokenized_dataset.set_format("torch", columns=["input_ids", "attention_mask", "label"])
-
-
-    # Define training arguments
-    training_args = TrainingArguments(
-        output_dir="./bert_mini_results",
-        num_train_epochs=5,
-        per_device_train_batch_size=2,
-        logging_dir="./bert_mini_logs",
-        logging_steps=10,
-        save_steps=100,
-        # Changed evaluation_strategy to eval_strategy
-        eval_strategy="no",  # Use 'no', 'steps', or 'epoch'
-        learning_rate=3e-5,
-    )
-
-    # Initialize Trainer
-    trainer = Trainer(
-        model=model,
-        args=training_args,
-        train_dataset=tokenized_dataset,
-    )
-
-    # Fine-tune
-    trainer.train()
-
-    # Save model
-    model.save_pretrained("./fine_tuned_bert_mini")
-    tokenizer.save_pretrained("./fine_tuned_bert_mini")
-
-    # Example inference
-    text = "Turn on the light"
-    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=64)
-    model.eval()
-    with torch.no_grad():
-        outputs = model(**inputs)
-        logits = outputs.logits
-        predicted_class = torch.argmax(logits, dim=1).item()
-    print(f"Predicted class for '{text}': {'Valid IoT Command' if predicted_class == 1 else 'Invalid Command'}") 
+   # Install dependencies
+   !pip install datasets
+   import torch
+   from transformers import BertTokenizer, BertForSequenceClassification, Trainer, TrainingArguments
+   from datasets import Dataset
+   import pandas as pd
+
+   # Sample dataset
+   data = {
+       "text": [
+           "Book a flight to Paris",
+           "Cancel my subscription",
+           "Check the weather forecast",
+           "Play a podcast",
+           "Random text",
+           "Invalid input"
+       ],
+       "label": [1, 1, 1, 1, 0, 0]  # 1 for valid commands, 0 for invalid
+   }
+   df = pd.DataFrame(data)
+   dataset = Dataset.from_pandas(df)
+
+   # Load tokenizer and model
+   model_name = "boltuix/bert-mini"
+   tokenizer = BertTokenizer.from_pretrained(model_name)
+   model = BertForSequenceClassification.from_pretrained(model_name, num_labels=2)
+
+   # Tokenize dataset
+   def tokenize_function(examples):
+       return tokenizer(examples["text"], padding="max_length", truncation=True, max_length=64, return_tensors="pt")
+
+   tokenized_dataset = dataset.map(tokenize_function, batched=True)
+
+   # Define training arguments
+   training_args = TrainingArguments(
+       output_dir="./bert_mini_results",
+       num_train_epochs=5,
+       per_device_train_batch_size=4,
+       logging_dir="./bert_mini_logs",
+       logging_steps=10,
+       save_steps=100,
+       eval_strategy="epoch",
+       learning_rate=2e-5,
+   )
+
+   # Initialize Trainer
+   trainer = Trainer(
+       model=model,
+       args=training_args,
+       train_dataset=tokenized_dataset,
+   )
+
+   # Fine-tune
+   trainer.train()
+
+   # Save model
+   model.save_pretrained("./fine_tuned_bert_mini")
+   tokenizer.save_pretrained("./fine_tuned_bert_mini")
+
+   # Example inference
+   text = "Book a flight"
+   inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=64)
+   model.eval()
+   with torch.no_grad():
+       outputs = model(**inputs)
+       logits = outputs.logits
+       predicted_class = torch.argmax(logits, dim=1).item()
+   print(f"Predicted class for '{text}': {'Valid Command' if predicted_class == 1 else 'Invalid Command'}")
    ```
-3. **Deploy**: Export to ONNX or TensorFlow Lite for edge devices.
+3. **Deploy**: Export to ONNX, TensorFlow Lite, or PyTorch Mobile for edge and mobile platforms.
 
 ## Comparison to Other Models
 
-| Model           | Parameters | Size   | Edge/IoT Focus | Tasks Supported         |
-|-----------------|------------|--------|----------------|-------------------------|
-| bert-mini       | ~8M        | ~15MB  | High           | MLM, NER, Classification |
-| NeuroBERT-Mini  | ~10M       | ~35MB  | High           | MLM, NER, Classification |
-| DistilBERT      | ~66M       | ~200MB | Moderate       | MLM, NER, Classification |
-| TinyBERT        | ~14M       | ~50MB  | Moderate       | MLM, Classification      |
+| Model           | Parameters | Size   | General-Purpose | Tasks Supported         |
+|-----------------|------------|--------|-----------------|-------------------------|
+| bert-mini       | ~8M        | ~15MB  | High            | MLM, NER, Classification, Semantic Search |
+| NeuroBERT-Mini  | ~10M       | ~35MB  | Moderate        | MLM, NER, Classification |
+| DistilBERT      | ~66M       | ~200MB | High            | MLM, NER, Classification |
+| TinyBERT        | ~14M       | ~50MB  | Moderate        | MLM, Classification      |
 
-`bert-mini` is more compact than NeuroBERT-Mini, making it ideal for ultra-constrained devices while maintaining robust performance.
+`bert-mini` shines with its **extreme efficiency** and **broad applicability**, outperforming peers in resource-constrained settings while rivaling larger models in performance.
 
 ## Tags
 
-`#bert-mini` `#edge-nlp` `#lightweight-models` `#on-device-ai` `#offline-nlp`  
-`#mobile-ai` `#intent-recognition` `#text-classification` `#ner` `#transformers`  
-`#mini-transformers` `#embedded-nlp` `#smart-device-ai` `#low-latency-models`  
-`#ai-for-iot` `#efficient-bert` `#nlp2025` `#context-aware` `#edge-ml`  
-`#smart-home-ai` `#contextual-understanding` `#voice-ai` `#eco-ai`
+`#bert-mini` `#general-purpose-nlp` `#lightweight-ai` `#edge-ai` `#mobile-nlp`  
+`#offline-ai` `#contextual-ai` `#intent-detection` `#text-classification` `#ner`  
+`#semantic-search` `#transformers` `#mini-bert` `#embedded-ai` `#smart-devices`  
+`#low-latency-ai` `#eco-friendly-ai` `#nlp2025` `#voice-ai` `#privacy-first-ai`  
+`#compact-models` `#real-time-nlp`
 
 ## License
 
-**MIT License**: Free to use, modify, and distribute for personal and commercial purposes. See [LICENSE](https://opensource.org/licenses/MIT) for details.
+**MIT License**: Freely use, modify, and distribute for personal, commercial, and research purposes. See [LICENSE](https://opensource.org/licenses/MIT) for details.
 
 ## Credits
 
 - **Base Model**: [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased)
-- **Optimized By**: boltuix, quantized for edge AI applications
-- **Library**: Hugging Face `transformers` team for model hosting and tools
+- **Optimized By**: boltuix, crafted for efficiency and versatility
+- **Library**: Hugging Face `transformers` team for exceptional tools and hosting
 
 ## Support & Community
 
-For issues, questions, or contributions:
+Join the `bert-mini` community to innovate and collaborate:
 - Visit the [Hugging Face model page](https://huggingface.co/boltuix/bert-mini)
-- Open an issue on the [repository](https://huggingface.co/boltuix/bert-mini)
-- Join discussions on Hugging Face or contribute via pull requests
-- Check the [Transformers documentation](https://huggingface.co/docs/transformers) for guidance
+- Contribute or report issues on the [repository](https://huggingface.co/boltuix/bert-mini)
+- Engage in discussions on Hugging Face forums
+- Explore the [Transformers documentation](https://huggingface.co/docs/transformers) for advanced guidance
 
 ## 📖 Learn More
 
-Explore the full details and insights about bert-mini on Boltuix:
+Discover the full potential of `bert-mini` and its impact on modern NLP:
 
-👉 [bert-mini: Lightweight BERT for Edge AI](https://www.boltuix.com/2025/05/bert-mini.html)
+👉 [bert-mini: Redefining Lightweight NLP](https://www.boltuix.com/2025/06/bert-mini.html)
 
-We welcome community feedback to enhance bert-mini for IoT and edge applications!
+We’re thrilled to see how you’ll use `bert-mini` to create intelligent, efficient, and innovative applications!