Text Classification
Transformers
Safetensors
English
bert
intent-classification
query-routing
agent
llm-router
text-embeddings-inference
Instructions to use ENTUM-AI/AgentRouter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ENTUM-AI/AgentRouter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ENTUM-AI/AgentRouter")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ENTUM-AI/AgentRouter") model = AutoModelForSequenceClassification.from_pretrained("ENTUM-AI/AgentRouter", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| language: | |
| - en | |
| license: apache-2.0 | |
| library_name: transformers | |
| tags: | |
| - text-classification | |
| - intent-classification | |
| - query-routing | |
| - agent | |
| - llm-router | |
| pipeline_tag: text-classification | |
| # β‘ AgentRouter | |
| Ultra-fast intent classification for LLM query routing. Classifies user queries into 10 intent categories in **<5ms** on GPU. | |
| Built on [MiniLM](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) (33M params) β small enough for CPU inference, fast enough for real-time routing. | |
| ## π Usage | |
| ```python | |
| from transformers import pipeline | |
| router = pipeline("text-classification", model="ENTUM-AI/AgentRouter") | |
| router("Write a Python function to sort a list") | |
| # [{'label': 'code_generation', 'score': 0.98}] | |
| router("Why am I getting a TypeError?") | |
| # [{'label': 'code_debugging', 'score': 0.97}] | |
| router("Translate hello to Spanish") | |
| # [{'label': 'translation', 'score': 0.99}] | |
| router("What is quantum computing?") | |
| # [{'label': 'information_retrieval', 'score': 0.96}] | |
| ``` | |
| ## π·οΈ Intent Classes | |
| | Intent | Description | Suggested Tools | | |
| |--------|-------------|----------------| | |
| | `code_generation` | Write new code | code_interpreter, file_editor | | |
| | `code_debugging` | Fix bugs and errors | code_interpreter, debugger | | |
| | `math_reasoning` | Solve math problems | calculator, wolfram_alpha | | |
| | `creative_writing` | Write stories, poems, essays | β | | |
| | `summarization` | Summarize text | file_reader | | |
| | `translation` | Translate between languages | translator | | |
| | `information_retrieval` | Answer questions, explain topics | knowledge_base | | |
| | `data_analysis` | Analyze data, create charts | code_interpreter, data_visualizer | | |
| | `web_search` | Search the web for current info | web_browser, search_engine | | |
| | `general_chat` | Casual conversation | β | | |
| ## π Use Cases | |
| - **LLM routing** β route queries to specialized models or tools | |
| - **Agent frameworks** β decide which tool to invoke | |
| - **Cost optimization** β use cheap models for simple intents, expensive for complex | |
| - **Latency optimization** β skip heavy pipelines for general chat | |
| ## β οΈ Limitations | |
| - English only | |
| - 10 fixed intent categories | |