NeuroBERT-Mini: A Beginner-Friendly Guide to Edge AI NLP

🧠 NeuroBERT-Mini: A Beginner-Friendly Guide to Edge AI NLP 🚀

NeuroBERT-Mini is a lightweight, real-time NLP model optimized for edge devices like smart assistants, microcontrollers, and embedded systems.

Derived from Google's BERT architecture, it offers efficient contextual language understanding in resource-constrained environments.

With a quantized size of ~35MB and ~7M parameters, it’s designed for low-latency and offline operation, making it ideal for privacy-first applications with limited connectivity. Hugging Face

✨ Key Features

Lightweight: ~35MB 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).

📊 Supported NLP Tasks

Task	Description	Hugging Face Pipeline
Masked Language Modeling	Predict missing words in sentences	`fill-mask`
Text Classification	Classify text into predefined categories	`text-classification`
Intent Detection	Identify user intent from input	`text-classification`
Named Entity Recognition	Detect and classify named entities in text	`ner`
Question Answering	Extract answers from context based on questions	`question-answering`

⚙️ Installation

Ensure your environment supports Python 3.6+ and has ~35MB of storage for model weights.

pip install transformers torch datasets scikit-learn pandas seqeval

📥 Loading the Model

You can load the model directly using the Hugging Face Transformers library:

from transformers import AutoModelForMaskedLM, AutoTokenizer

model_name = "boltuix/NeuroBERT-Mini"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForMaskedLM.from_pretrained(model_name)

🚀 Quickstart Examples

1. Masked Language Modeling
Predict missing words in sentences:

from transformers import pipeline

mask_filler = pipeline("fill-mask", model="boltuix/NeuroBERT-Mini", tokenizer="boltuix/NeuroBERT-Mini")
sentence = "The smart fan will [MASK] automatically when it gets hot."
results = mask_filler(sentence)

for r in results:
    print(f"Prediction: {r['token_str']}, Score: {r['score']:.4f}")

# Example Output:
# Prediction: turn, Score: 0.4212
# Prediction: switch, Score: 0.2013
# Prediction: shut, Score: 0.1537
# Prediction: activate, Score: 0.0912
# Prediction: run, Score: 0.0654

2. Intent Classification
Fine-tune and classify text into intents (e.g., greeting, turn_off_fan):

# Install required packages before running:
# pip install transformers datasets scikit-learn pandas

import pandas as pd
from sklearn.model_selection import train_test_split
from datasets import Dataset
from transformers import (
    AutoTokenizer, AutoModelForSequenceClassification, Trainer,
    TrainingArguments, EarlyStoppingCallback
)
from sklearn.metrics import accuracy_score

# Step 1: Define dataset with intents
intents_data = {
    "greeting": [
        "Hello", "Hi there", "Hey", "Good morning", "Good evening", "How are you?",
        "Nice to meet you", "Howdy", "Hey there", "Yo",
        "Hi, assistant", "Greetings", "Hi buddy", "Good day", "Welcome back",
        "Hello again", "Hey assistant", "Hi friend", "Nice seeing you", "What's up"
    ],
    "turn_off_fan": [
        "Turn off the fan", "Please switch off the fan", "Can you turn the fan off?",
        "Stop the fan", "Fan off", "Shut down the fan", "Disable the fan",
        "I want the fan off", "Kill the fan", "Turn the fan off now",
        "Cut the fan", "Fan should be off", "Make the fan stop", "Fan off please",
        "Deactivate fan", "Turn that fan off", "Power down the fan", "Stop spinning fan",
        "Fan needs to go off", "Turn off ceiling fan"
    ],
    "turn_on_light": [
        "Turn on the light", "Switch on the lights", "Light on please",
        "Enable the lights", "Lights up", "Please turn on light",
        "Make it bright", "Illuminate the room", "Power on the light",
        "Start the lights", "I need lights on", "Activate light",
        "Turn lights on", "Can you switch on light?", "Lights, please",
        "Turn that light on", "Wake up the lights", "Brighten the room",
        "Let there be light", "Make room visible"
    ],
    "weather_query": [
        "What's the weather today?", "Will it rain?", "Tell me the weather forecast",
        "How's the weather?", "Give me today's weather", "Is it sunny?",
        "Will it be cloudy?", "Weather update", "Forecast for today",
        "Any chance of rain?", "Show me weather", "Is it going to snow?",
        "Do I need an umbrella?", "Weather news", "Will it be hot today?",
        "Is it cold outside?", "Weather check", "Current weather status",
        "What's the temperature?", "Temperature outside now?"
    ],
    "goodbye": [
        "Goodbye", "Bye", "See you later", "Catch you later",
        "Talk to you soon", "Farewell", "I'm leaving", "Take care",
        "Until next time", "Later", "See ya", "Bye-bye", "Peace out",
        "Gotta go", "End chat", "That's all", "Over and out",
        "Catch you next time", "Talk later", "Quit now"
    ]
}

# Flatten dataset
examples = [(text, intent) for intent, texts in intents_data.items() for text in texts]
df = pd.DataFrame(examples, columns=["text", "label"])

# Encode labels
label2id = {label: idx for idx, label in enumerate(df["label"].unique())}
id2label = {idx: label for label, idx in label2id.items()}
df["label_id"] = df["label"].map(label2id)

# Split into train and validation
train_df, val_df = train_test_split(df, test_size=0.2, stratify=df["label_id"], random_state=42)
train_dataset = Dataset.from_pandas(train_df[["text", "label_id"]])
val_dataset = Dataset.from_pandas(val_df[["text", "label_id"]])

# Step 2: Load model and tokenizer
model_name = "boltuix/NeuroBERT-Mini"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(
    model_name,
    num_labels=len(label2id),
    id2label=id2label,
    label2id=label2id
)

# Step 3: Tokenization
def tokenize(batch):
    tokenized_inputs = tokenizer(batch["text"], truncation=True, padding=True)
    tokenized_inputs["labels"] = batch["label_id"]
    return tokenized_inputs

train_dataset = train_dataset.map(tokenize, batched=True)
val_dataset = val_dataset.map(tokenize, batched=True)

# Step 4: Define training arguments
training_args = TrainingArguments(
    output_dir="./intent_model",
    eval_strategy="epoch",
    save_strategy="epoch",
    learning_rate=2e-5,
    per_device_train_batch_size=16,
    per_device_eval_batch_size=16,
    num_train_epochs=5,
    weight_decay=0.01,
    load_best_model_at_end=True,
    metric_for_best_model="accuracy"
)

# Metrics
def compute_metrics(eval_pred):
    predictions = eval_pred.predictions.argmax(-1)
    return {"accuracy": accuracy_score(eval_pred.label_ids, predictions)}

# Step 5: Trainer setup
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=val_dataset,
    compute_metrics=compute_metrics,
    tokenizer=tokenizer,
    callbacks=[EarlyStoppingCallback(early_stopping_patience=2)]
)

# Step 6: Train and Save
trainer.train()
trainer.save_model("./fine-tuned-NeuroBERT-Mini-intents")

# Step 7: Inference
from transformers import pipeline
model_path = "./fine-tuned-NeuroBERT-Mini-intents"
classifier = pipeline("text-classification", model=model_path)

test_sentences = [
    "Hello",
    "Can you turn off the fan?",
    "Turn on the light",
    "What's the weather today?",
    "Bye"
]

for text in test_sentences:
    result = classifier(text)[0]
    print(f"🧑 You: {text}")
    print(f"🤖 Bot ({result['label']} - {result['score']:.2f}): Intent recognized\n")

3. Named Entity Recognition (NER)
Fine-tune and identify named entities in text:

from transformers import (
    AutoTokenizer, AutoModelForTokenClassification,
    DataCollatorForTokenClassification, Trainer,
    TrainingArguments, EarlyStoppingCallback, pipeline
)
from datasets import Dataset
import numpy as np

# ✅ Define 10 IoT sample data points
samples = [
    {"tokens": ["Turn", "on", "the", "kitchen", "light"], "ner_tags": [0, 0, 0, 1, 2]},
    {"tokens": ["Switch", "off", "bedroom", "fan"], "ner_tags": [0, 0, 1, 2]},
    {"tokens": ["Open", "the", "garage", "door"], "ner_tags": [0, 0, 1, 2]},
    {"tokens": ["Close", "the", "window"], "ner_tags": [0, 0, 1]},
    {"tokens": ["Set", "thermostat", "to", "22", "degrees"], "ner_tags": [0, 1, 0, 0, 0]},
    {"tokens": ["Play", "jazz", "in", "living", "room"], "ner_tags": [0, 1, 0, 1, 2]},
    {"tokens": ["Dim", "the", "dining", "room", "lights"], "ner_tags": [0, 0, 1, 2, 2]},
    {"tokens": ["Lock", "the", "front", "door"], "ner_tags": [0, 0, 1, 2]},
    {"tokens": ["Start", "the", "coffee", "machine"], "ner_tags": [0, 0, 1, 2]},
    {"tokens": ["Turn", "off", "garden", "sprinkler"], "ner_tags": [0, 0, 1, 2]},
]

# ✅ Define labels
label_list = ["O", "DEVICE", "ACTION"]  # 0 = O, 1 = DEVICE, 2 = ACTION
label2id = {l: i for i, l in enumerate(label_list)}
id2label = {i: l for l, i in label2id.items()}

# ✅ Adjust label indices for consistency
for sample in samples:
    sample["ner_tags"] = [int(tag) for tag in sample["ner_tags"]]

# ✅ Convert to Hugging Face dataset
dataset = Dataset.from_list(samples).train_test_split(test_size=0.2)
tokenizer = AutoTokenizer.from_pretrained("boltuix/NeuroBERT-Mini")

# ✅ Load model
model = AutoModelForTokenClassification.from_pretrained(
    "boltuix/NeuroBERT-Mini",
    num_labels=len(label_list),
    id2label=id2label,
    label2id=label2id
)

# ✅ Tokenize and align labels
def tokenize_and_align_labels(examples):
    tokenized = tokenizer(examples["tokens"], truncation=True, padding=True, is_split_into_words=True)
    labels = []
    for i, label in enumerate(examples["ner_tags"]):
        word_ids = tokenized.word_ids(batch_index=i)
        previous_word_idx = None
        label_ids = []
        for word_idx in word_ids:
            if word_idx is None:
                label_ids.append(-100)
            elif word_idx != previous_word_idx:
                label_ids.append(label[word_idx])
            else:
                label_ids.append(label[word_idx])
            previous_word_idx = word_idx
        labels.append(label_ids)
    tokenized["labels"] = labels
    return tokenized

# ✅ Tokenize
tokenized_ds = dataset.map(tokenize_and_align_labels, batched=True)
data_collator = DataCollatorForTokenClassification(tokenizer)

# ✅ Training args
training_args = TrainingArguments(
    output_dir="./ner_model",
    eval_strategy="epoch",
    save_strategy="epoch",
    per_device_train_batch_size=2,
    num_train_epochs=10,
    learning_rate=5e-5,
    weight_decay=0.01,
    logging_steps=5,
    load_best_model_at_end=True
)

# ✅ Train the model
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_ds["train"],
    eval_dataset=tokenized_ds["test"],
    tokenizer=tokenizer,
    data_collator=data_collator,
    callbacks=[EarlyStoppingCallback(early_stopping_patience=2)]
)

trainer.train()
trainer.save_model("neurobert-mini-iot-ner")

# ✅ Inference
ner = pipeline("token-classification", model="neurobert-mini-iot-ner", tokenizer=tokenizer, aggregation_strategy="simple")
print(ner("Turn on the garden lights when someone enters the backyard."))

4. Question Answering
Fine-tune and extract answers from context:

from datasets import Dataset
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
from transformers import TrainingArguments, Trainer, default_data_collator
from transformers import pipeline

# 1. Prepare a small custom QA dataset
data = {
    "id": ["1", "2"],
    "title": ["Example1", "Example2"],
    "context": [
        "Paris is the capital of France.",
        "The Eiffel Tower is a famous landmark in Paris."
    ],
    "question": [
        "What is the capital of France?",
        "Where is the Eiffel Tower located?"
    ],
    "answers": [
        {"text": ["Paris"], "answer_start": [0]},
        {"text": ["Paris"], "answer_start": [31]}
    ]
}

custom_dataset = Dataset.from_dict(data)

# 2. Load model and tokenizer
model_name = "boltuix/NeuroBERT-Mini"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForQuestionAnswering.from_pretrained(model_name)

# 3. Preprocessing function to tokenize and align answer spans
def preprocess(examples):
    tokenized = tokenizer(
        examples["question"],
        examples["context"],
        truncation="only_second",
        max_length=384,
        stride=128,
        return_overflowing_tokens=True,
        return_offsets_mapping=True,
        padding="max_length"
    )
    
    sample_mapping = tokenized.pop("overflow_to_sample_mapping")
    offset_mapping = tokenized.pop("offset_mapping")
    
    start_positions = []
    end_positions = []
    
    for i, offsets in enumerate(offset_mapping):
        sample_index = sample_mapping[i]
        answers = examples["answers"][sample_index]
        answer_starts = answers["answer_start"]
        answer_texts = answers["text"]
        
        if len(answer_starts) == 0:
            # No answer given
            start_positions.append(tokenizer.cls_token_id)
            end_positions.append(tokenizer.cls_token_id)
        else:
            start_char = answer_starts[0]
            end_char = start_char + len(answer_texts[0])
            
            # Find start token index
            token_start_index = 0
            while token_start_index < len(offsets) and offsets[token_start_index][0] <= start_char:
                token_start_index += 1
            token_start_index -= 1
            
            # Find end token index
            token_end_index = len(offsets) - 1
            while token_end_index >= 0 and offsets[token_end_index][1] >= end_char:
                token_end_index -= 1
            token_end_index += 1
            
            start_positions.append(token_start_index)
            end_positions.append(token_end_index)
    
    tokenized["start_positions"] = start_positions
    tokenized["end_positions"] = end_positions
    
    return tokenized

# 4. Split dataset: train on first example, validate on second
train_dataset = custom_dataset.select([0])
eval_dataset = custom_dataset.select([1])

tokenized_train = train_dataset.map(preprocess, batched=True, remove_columns=train_dataset.column_names)
tokenized_eval = eval_dataset.map(preprocess, batched=True, remove_columns=eval_dataset.column_names)

# 5. Training arguments and Trainer
training_args = TrainingArguments(
    output_dir="./qa_model",
    eval_strategy="epoch", 
    save_strategy="epoch",
    learning_rate=2e-5,
    per_device_train_batch_size=1,
    num_train_epochs=3,
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_train,
    eval_dataset=tokenized_eval,
    tokenizer=tokenizer,
    data_collator=default_data_collator
)

# 6. Train and save
trainer.train()
trainer.save_model("neurobert-mini-qa")

# 7. Inference test
qa_pipeline = pipeline("question-answering", model="neurobert-mini-qa", tokenizer="neurobert-mini-qa")
result = qa_pipeline(
    question="What is the capital of France?",
    context="France's capital is Paris."
)
print(result)

🧪 Evaluation

NeuroBERT-Mini was evaluated on a masked language modeling task using 10 IoT-related sentences. 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.

Sample Results:

Sentence: "She is a [MASK] at the local hospital."
Expected: nurse
Top-5 Predictions: doctor, nurse, surgeon, technician, assistant
Result: ✅ PASS

Sentence: "Turn off the lights after [MASK] minutes."
Expected: five
Top-5 Predictions: ten, two, three, fifteen, twenty
Result: ❌ FAIL

Total Passed: ~8/10 (depends on fine-tuning).

💡 Use Cases

Smart Home Devices: Parse commands like “Turn [MASK] the coffee machine” (predicts “on”) or “The fan will turn [MASK]” (predicts “off”).
IoT Sensors: Interpret sensor contexts, e.g., “The drone collects data using onboard [MASK]” (predicts “sensors”).
Wearables: Real-time intent detection, e.g., “The music pauses when someone [MASK] the room” (predicts “enters”).
Mobile Apps: Offline chatbots or semantic search, e.g., “She is a [MASK] at the hospital” (predicts “nurse”).
Voice Assistants: Local command parsing, e.g., “Please [MASK] the door” (predicts “shut”).

🖥️ Hardware Requirements

Processors: CPUs, mobile NPUs, or microcontrollers (e.g., ESP32, Raspberry Pi)
Storage: ~35MB for model weights (quantized for reduced footprint)
Memory: ~80MB RAM for inference
Environment: Offline or low-connectivity settings

📚 Training Data

NeuroBERT-Mini was trained on a custom IoT dataset focused on IoT terminology, smart home commands, and sensor-related contexts. This enhances performance on tasks like command parsing and device control. Fine-tuning on domain-specific data is recommended for optimal results.

🔧 Fine-Tuning Guide

To adapt NeuroBERT-Mini for custom IoT tasks (e.g., specific smart home commands):

Prepare Dataset: Collect labeled data (e.g., commands with intents or masked sentences).
Fine-Tune with Hugging Face: Use the Transformers library to fine-tune the model on your dataset.
Deploy: Export the fine-tuned model to ONNX or TensorFlow Lite for edge devices.

⚖️ Comparison to Other Models

Model	Parameters	Size	Edge/IoT Focus	Tasks Supported
NeuroBERT-Mini	~10M	~35MB	High	MLM, NER, Classification, QA
NeuroBERT-Tiny	~5M	~15MB	Medium	MLM, NER, Classification
DistilBERT	~66M	~200MB	Moderate	MLM, NER, Classification
TinyBERT	~14M	~50MB	Moderate	MLM, Classification

NeuroBERT-Mini offers a balance between size and performance, making it ideal for edge devices with slightly more resources than those targeted by NeuroBERT-Tiny.

📄 License

MIT License: Free to use, modify, and distribute for personal and commercial purposes. See LICENSE for details.

🙏 Credits

Base Model: google-bert/bert-base-uncased
Optimized By: boltuix, quantized for edge AI applications
Library: Hugging Face Transformers team for model hosting and tools

💬 Support & Community

For issues, questions, or contributions:

Visit the Hugging Face model page: boltuix/NeuroBERT-Mini
Open an issue on the repository
Join discussions on Hugging Face or contribute via pull requests
Check the Transformers documentation for guidance

We welcome community feedback to enhance NeuroBERT-Mini for IoT and edge applications!

❓ FAQ

Q1: What tasks does NeuroBERT-Mini support?
A1: It supports masked language modeling, text classification, intent detection, named entity recognition, and question answering.

Q2: Is NeuroBERT-Mini suitable for real-time applications?
A2: Yes, it's optimized for low-latency inference on edge devices.

Q3: Can I fine-tune NeuroBERT-Mini on my own dataset?
A3: Absolutely! The model is designed for easy fine-tuning using Hugging Face’s Transformers library. This allows adapting it to specific domains or tasks like custom intent detection or specialized NER.

Q4: What programming languages and frameworks are supported?
A4: The model works primarily with Python via the Transformers library. For deployment, it can be converted to ONNX, TensorFlow Lite, or CoreML for integration into various platforms (mobile, embedded systems).

Q5: How does NeuroBERT-Mini handle multi-language input?
A5: It is trained mainly on English datasets. For multilingual support, fine-tuning on other languages or using multilingual variants is recommended.

🚀 Next Steps

Download and try it out: Explore the model at Hugging Face
Fine-tune on your domain: Customize it with your IoT or edge-related data
Integrate on edge devices: Convert and deploy with ONNX or TFLite for low-latency offline NLP
Contribute: Share your enhancements or datasets to improve the model ecosystem

🚀 Notes on Fine-Tuning and Dataset Requirements for NeuroBERT-Mini

🔧 Fine-tuning is necessary:
The base NeuroBERT-Mini model is pretrained on general language tasks but not fine-tuned on specific tasks like NER or QA.
You must fine-tune it on labeled task-specific data to get good results.
📊 Dataset size and quality:
- For quick tests, small datasets (tens to hundreds of samples) can work but accuracy will be limited.
- For decent accuracy, aim for thousands of labeled examples (e.g., 1k–3k for NER, 5k+ for QA).
- Larger, diverse datasets improve model generalization and performance.
⏳ Training considerations:
- More epochs and larger batch sizes help but beware of overfitting on small datasets.
- Use a validation set to monitor progress.
- Apply early stopping or checkpoints to save the best model.
🛠️ Custom datasets:
- If public datasets don’t fit your needs (e.g., IoT domain), create your own labeled dataset.
- Quality annotation and clear labeling are crucial.
🧠 Model architecture:
- NeuroBERT-Mini is a smaller, lightweight BERT variant designed for edge devices.
- May require more data/fine-tuning steps than larger models to match their accuracy.
- Optimized for speed and size, with some trade-off in max accuracy.
📏 Recommended minimum dataset sizes (approximate):
- NER: 1,000–3,000 annotated sentences
- QA: 5,000+ question-context-answer triplets
🎯 Summary:
Fine-tuning on a sufficiently large, high-quality dataset is 🔑 to improve NeuroBERT-Mini’s performance. For domain-specific tasks, custom datasets are recommended. Always validate and use early stopping for best results.

🎉 Thank You for Using NeuroBERT-Mini!

Empowering smarter IoT and edge AI applications with efficient, context-aware NLP — right where it matters most.

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