Text Classification
Transformers
ONNX
Safetensors
English
distilbert
intent-classification
multitask
iab
conversational-ai
adtech
calibrated-confidence
text-embeddings-inference
Instructions to use admesh/agentic-intent-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use admesh/agentic-intent-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="admesh/agentic-intent-classifier")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("admesh/agentic-intent-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| # Roadmap Issue Drafts | |
| These are the next three roadmap issues to open in GitHub once authenticated issue creation is available. | |
| ## 1. Build External Demo UI For Decision Envelope | |
| Suggested title: | |
| `Build external demo UI for query -> model_output -> system_decision` | |
| Suggested body: | |
| ```md | |
| ## Goal | |
| Add a simple external-facing demo interface on top of `/classify` so a user can paste a query and see the full decision envelope in a clean, understandable format. | |
| ## Scope | |
| - add a lightweight UI for entering a raw query | |
| - render `model_output.classification.intent` | |
| - render fallback state when present | |
| - render `system_decision.policy` | |
| - render `system_decision.opportunity` | |
| - include a few preloaded demo prompts | |
| ## Why | |
| The current JSON API is enough for engineering validation, but not enough for partner demos or taxonomy walkthroughs. | |
| ## Done When | |
| - someone can run the demo locally and inspect the full output without using curl | |
| - the UI clearly shows query -> classification -> system decision | |
| ``` | |
| ## 2. Add Better Support Handling To Intent-Type Layer | |
| Suggested title: | |
| `Add dedicated support handling to reduce personal_reflection fallback on account-help prompts` | |
| Suggested body: | |
| ```md | |
| ## Goal | |
| Reduce the current failure mode where support-like prompts such as login and billing issues collapse into `personal_reflection` or low-confidence fallback behavior. | |
| ## Scope | |
| - review support-like prompts in the current benchmark | |
| - decide whether to add a dedicated `support` intent-type head or a rule-based override layer | |
| - add a fixed support-oriented evaluation set | |
| - document the chosen approach in `known_limitations.md` | |
| ## Why | |
| The `decision_phase` head can already separate `support` reasonably well, but the `intent_type` layer still underperforms on these cases. | |
| ## Done When | |
| - support prompts are no longer commonly labeled as `personal_reflection` | |
| - the combined envelope fails safe for support queries with clearer semantics | |
| ``` | |
| ## 3. Add Evaluation Harness And Canonical Benchmark Runner | |
| Suggested title: | |
| `Add canonical benchmark runner for demo prompts and regression checks` | |
| Suggested body: | |
| ```md | |
| ## Goal | |
| Turn the current prompt suite and canonical examples into a repeatable regression harness. | |
| ## Scope | |
| - add a script that runs the fixed demo prompts through `combined_inference.py` | |
| - save outputs to a machine-readable artifact | |
| - compare current outputs against expected behavior notes | |
| - flag meaningful regressions in fallback behavior and phase classification | |
| ## Why | |
| The repo now has frozen `v0.1` baselines. A benchmark runner is the clean way to protect demo quality without returning to ad hoc tuning. | |
| ## Done When | |
| - one command runs the prompt suite end to end | |
| - current outputs are easy to inspect and compare over time | |
| - demo regressions become visible before external sharing | |
| ``` | |