admesh/agentic-intent-classifier

Production-ready intent + IAB classifier bundle for conversational traffic.

Combines multitask intent modeling, supervised IAB content classification, and per-head confidence calibration to support safe monetization decisions in real time.

Links

• Hugging Face: https://huggingface.co/admesh/agentic-intent-classifier
• GitHub: https://github.com/GouniManikumar12/agentic-intent-classifier

What It Predicts

Field	Description
intent.type	commercial, informational, navigational, transactional, …
intent.subtype	product_discovery, comparison, how_to, …
intent.decision_phase	awareness, consideration, decision, …
iab_content	IAB Content Taxonomy 3.0 tier1 / tier2 / tier3 labels
component_confidence	Per-head calibrated confidence with threshold flags
system_decision	Monetization eligibility, opportunity type, policy

---

Deployment Options

0. Colab / Kaggle Quickstart (copy/paste)

!pip -q install -U pip
!pip -q install -U "torch==2.10.0" "torchvision==0.25.0" "torchaudio==2.10.0"
!pip -q install -U "transformers>=4.36.0" "huggingface_hub>=0.20.0" "safetensors>=0.4.0"

Restart the runtime after installs (Runtime → Restart runtime) so the new Torch version is actually used.

from transformers import pipeline

clf = pipeline(
    "admesh-intent",
    model="admesh/agentic-intent-classifier",
    trust_remote_code=True,  # required (custom pipeline + multi-model bundle)
)

out = clf("Which laptop should I buy for college?")
print(out["meta"])
print(out["model_output"]["classification"]["intent"])

---

Latency / inference timing (quick check)

The first call includes model/code loading. Warm up once, then measure:

import time
q = "Which laptop should I buy for college?"

_ = clf("warm up")
t0 = time.perf_counter()
out = clf(q)
print(f"latency_ms={(time.perf_counter() - t0) * 1000:.1f}")

1. transformers.pipeline() — anywhere (Python)

from transformers import pipeline

clf = pipeline(
    "admesh-intent",
    model="admesh/agentic-intent-classifier",
    trust_remote_code=True,
)

result = clf("Which laptop should I buy for college?")

Batch and custom thresholds:

# batch
results = clf([
    "Best running shoes under $100",
    "How does TCP work?",
    "Buy noise-cancelling headphones",
])

# custom confidence thresholds
result = clf(
    "Buy headphones",
    threshold_overrides={"intent_type": 0.6, "intent_subtype": 0.35},
)

---

2. HF Inference Endpoints (managed, deploy to AWS / Azure / GCP)

1. Go to https://ui.endpoints.huggingface.co
2. New Endpoint → select admesh/agentic-intent-classifier
3. Framework: PyTorch — Task: Text Classification
4. Enable "Load with trust_remote_code"
5. Deploy

The endpoint serves the same pipeline() interface above via REST:

curl https://<your-endpoint>.endpoints.huggingface.cloud \
  -H "Authorization: Bearer $HF_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"inputs": "Which laptop should I buy for college?"}'

---

3. HF Spaces (Gradio / Streamlit demo)

# app.py for a Gradio Space
import gradio as gr
from transformers import pipeline

clf = pipeline(
    "admesh-intent",
    model="admesh/agentic-intent-classifier",
    trust_remote_code=True,
)

def classify(text):
    return clf(text)

gr.Interface(fn=classify, inputs="text", outputs="json").launch()

---

4. Local / notebook via snapshot_download

import sys
from huggingface_hub import snapshot_download

local_dir = snapshot_download(
    repo_id="admesh/agentic-intent-classifier",
    repo_type="model",
)
sys.path.insert(0, local_dir)

from pipeline import AdmeshIntentPipeline
clf = AdmeshIntentPipeline()
result = clf("I need a CRM for a 5-person startup")

Or the one-liner factory:

from pipeline import AdmeshIntentPipeline
clf = AdmeshIntentPipeline.from_pretrained("admesh/agentic-intent-classifier")

---

Troubleshooting (avoid environment errors)

No module named 'combined_inference' (or similar)

This means the Hub repo root is missing required Python files. Ensure these exist at the root of the model repo (same level as pipeline.py):

• pipeline.py, config.json, config.py
• combined_inference.py, schemas.py
• model_runtime.py, multitask_runtime.py, multitask_model.py
• inference_intent_type.py, inference_subtype.py, inference_decision_phase.py, inference_iab_classifier.py
• iab_classifier.py, iab_taxonomy.py

does not appear to have a file named model.safetensors

Transformers requires a standard checkpoint at the repo root for pipeline() to initialize. This repo includes a small dummy model.safetensors + tokenizer files at the root for compatibility; the real production weights live in:

• multitask_intent_model_output/
• iab_classifier_model_output/
• artifacts/calibration/

---

Example Output

{
  "model_output": {
    "classification": {
      "iab_content": {
        "taxonomy": "IAB Content Taxonomy",
        "taxonomy_version": "3.0",
        "tier1": {"id": "552", "label": "Style & Fashion"},
        "tier2": {"id": "579", "label": "Men's Fashion"},
        "mapping_mode": "exact",
        "mapping_confidence": 0.73
      },
      "intent": {
        "type": "commercial",
        "subtype": "product_discovery",
        "decision_phase": "consideration",
        "confidence": 0.9549,
        "commercial_score": 0.656
      }
    }
  },
  "system_decision": {
    "policy": {
      "monetization_eligibility": "allowed_with_caution",
      "eligibility_reason": "commercial_discovery_signal_present"
    },
    "opportunity": {"type": "soft_recommendation", "strength": "medium"}
  },
  "meta": {
    "system_version": "0.6.0-phase4",
    "calibration_enabled": true,
    "iab_mapping_is_placeholder": false
  }
}

Reproducible Revision

from huggingface_hub import snapshot_download
local_dir = snapshot_download(
    repo_id="admesh/agentic-intent-classifier",
    repo_type="model",
    revision="0584798f8efee6beccd778b0afa06782ab5add60",
)

Included Artifacts

Path	Contents
multitask_intent_model_output/	DistilBERT multitask weights + tokenizer
iab_classifier_model_output/	IAB content classifier weights + tokenizer
artifacts/calibration/	Per-head temperature + threshold JSONs
pipeline.py	AdmeshIntentPipeline (transformers.Pipeline subclass)
combined_inference.py	Core inference logic

Notes

• trust_remote_code=True is required because this model uses a custom multi-head architecture that does not map to a single standard AutoModel checkpoint.
• meta.iab_mapping_is_placeholder: true means IAB artifacts were missing or skipped; train and calibrate IAB for full production accuracy.
• For long-running servers, instantiate once and reuse — models are cached in memory after the first call.