Text Classification
Transformers
Safetensors
distilbert
multilingual
nigeria
intent-classification
language-routing
text-embeddings-inference
Instructions to use tarvico/sireiq_voice with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tarvico/sireiq_voice with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tarvico/sireiq_voice")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tarvico/sireiq_voice") model = AutoModelForSequenceClassification.from_pretrained("tarvico/sireiq_voice") - Notebooks
- Google Colab
- Kaggle
Nigeria Router Model
A multilingual text classification model for Nigerian user routing systems.
This model predicts:
- User language
- User intent
Combined labels examples:
- en_check_balance
- pcm_check_balance
- ha_transfer
- yo_transfer
- ig_check_balance
Supported Languages
- English (
en) - Nigerian Pidgin (
pcm) - Hausa (
ha) - Yoruba (
yo) - Igbo (
ig)
Supported Intents
- check_balance
- transfer
Base Model
Fine-tuned from:
- distilbert-base-multilingual-cased
Use Case
Designed for:
- Voice assistants
- Customer support bots
- Banking chatbots
- Language routing systems
- Multilingual automation in Nigeria
Quick Start
from transformers import pipeline
clf = pipeline(
"text-classification",
model="YOUR_USERNAME/nigeria-router-model"
)
print(clf("abeg check my balance"))
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