Text Classification
Transformers
Safetensors
English
roberta
router
intent-classification
distilroberta
text-embeddings-inference
Instructions to use atekrugis/distilroberta-base-v4-gold-router with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use atekrugis/distilroberta-base-v4-gold-router with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="atekrugis/distilroberta-base-v4-gold-router")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("atekrugis/distilroberta-base-v4-gold-router") model = AutoModelForSequenceClassification.from_pretrained("atekrugis/distilroberta-base-v4-gold-router", device_map="auto") - Notebooks
- Google Colab
- Kaggle
DistilRoBERTa V4 Gold Router
This model is a high-precision intent router designed to classify user prompts into 6 distinct categories for LLM orchestration.
π Categories & Label Mapping
- 0: agentic (Weather, searches, real-time data)
- 1: coding (Programming tasks, debugging)
- 2: general (Chitchat, greetings, basic info)
- 3: math (Calculations, LaTeX, logic)
- 4: reasoning (Chain-of-thought, philosophy, deep analysis)
- 5: tool_calling (API structured requests)
π οΈ Training Details
- Base Model: distilroberta-base
- Dataset: V4 Gold Purified (approx. 46k rows)
- Accuracy: 99.8% on validation set
- Key Fixes: Resolved the "Reasoning Bias" found in V3 and stabilized against NaN gradient explosions.
- Downloads last month
- 9