from sentence_transformers import SentenceTransformer Load the model model = SentenceTransformer("icoquangninh/eto-taxi-model") Generate embeddings embeddings = model.encode(["Nhà hàng ABC", "Khách sạn XYZ"]) Calculate similarity from sklearn.metrics.pairwise import cosine_similarity similarity = cosine_similarity([embeddings[0]], [embeddings[1]])

Training Data

The model was trained on Vietnamese taxi booking data to understand location names and pricing contexts.

Evaluation Results

The model achieves good performance on Vietnamese text understanding tasks related to taxi services.

Intended Uses & Limitations

This model is intended for:

Extracting location information from Vietnamese text
Calculating similarity between locations
Pricing estimation for taxi services

Limitations:

Model is specifically trained for Vietnamese language
Performance may vary for locations outside Vietnam
Pricing estimations are based on historical data patterns

Training Data

The model was trained on Vietnamese taxi booking data to understand location names and pricing contexts. The training data includes:

Vietnamese location names and addresses
Taxi route information
Pricing data for various distances

Evaluation Results

The model achieves good performance on Vietnamese text understanding tasks related to taxi services with high accuracy in location name recognition and similarity calculation.

Applications

This model can be used for:

Location-based search in taxi booking applications
Route optimization and pricing calculation
Similar location recommendation
Customer service automation for taxi companies