🗺️ bert-trip-plan: Trip Planning Entity Recognition 🚉✈️🚗

🚀 Overview

bert-trip-plan is a BERT-based model fine-tuned specifically for Trip Planning Entity Recognition (NER) 🧭. It accurately extracts key entities like "From", "To", and "Mode" from natural language travel queries, making it ideal for use in travel assistants 🤖, chatbots 💬, and navigation systems 🗺️.

🎯 Example

• Input: "I want to travel from New York to Chicago by train."
• Output:
  • 📍 "New York" → From
  • 📍 "Chicago" → To
  • 🚆 "train" → Mode

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🌟 Key Features

✨ Feature	📌 Description
🎯 Task	Named Entity Recognition (NER) specialized for trip planning queries.
🔍 Extracted Entities	From (origin), To (destination), and Mode (transportation method).
🤖 Model	BERT fine-tuned for token classification tasks.
🌐 Language	English

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🛠️ Usage

You can easily integrate bert-trip-plan with Hugging Face’s Transformers library. Below are two examples demonstrating how to use the model.

✅ Basic Example

from transformers import pipeline

# 🤖 Load the model
ner_pipeline = pipeline("token-classification", model="boltuix/bert-trip-plan", aggregation_strategy="simple")

# 📝 Input travel query
query = "I want to travel from New York to Chicago by train."

# 🧠 Perform NER
results = ner_pipeline(query)

# 📤 Display extracted entities
print(results)

🔎 Sample Output

[
  {"entity_group": "from_loc", "word": "New York", "score": 0.999},
  {"entity_group": "to_loc", "word": "Chicago", "score": 0.998},
  {"entity_group": "transport_mode", "word": "train", "score": 0.997}
]

---

💡 Trip Planning Example

from transformers import pipeline
import json

# 🚀 Load the NER model for trip planning
ner_pipeline = pipeline("token-classification", model="boltuix/bert-trip-plan", aggregation_strategy="simple")

# 🧾 Input query
query = "plan a trip to New York from San Francisco by flight"

# 🧠 Perform Named Entity Recognition
results = ner_pipeline(query)

# 📦 Initialize output dictionary
output = {
    'from': '',
    'to': '',
    'mode': ''
}

# 🧹 Extract entities
for item in results:
    entity = item['entity_group']
    word = item['word'].strip('.').strip()

    if entity == 'from_loc':
        output['from'] = word
    elif entity == 'to_loc':
        output['to'] = word
    elif entity == 'transport_mode':
        output['mode'] = word

# 🖨️ Print structured output
print(json.dumps(output, indent=2))

📣 Let us know if you want to see:
- 🌐 A live demo
- ⚙️ API integration
- 🧩 UI wrapper with Gradio or Streamlit
- 📊 Evaluation metrics & benchmark comparisons

Happy Planning! 🌍✈️🚄🧳