---
license: apache-2.0
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
pipeline_tag: text-classification
language: [en, es, fr, de, zh, nl, it, ar]
tags: [intent-classification, logistics, multilingual, open-set, text-classification]
model-index:
- name: intent-classifier-mpnet
  results:
  - task: {type: text-classification, name: Intent classification}
    dataset: {name: synthetic logistics intents (500 rows, private), type: private}
    metrics:
    - {type: f1, name: macro-F1 (test), value: 0.8848}
    - {type: accuracy, name: accuracy (test), value: 0.8961}
---

# intent-classifier-mpnet

Routes a short user message from a logistics / supply-chain chat assistant to one of **12 intents**, and abstains with **`unknown`** when it is not confident (calibrated reject rule stored in `config.json`).
Fine-tuned from [`sentence-transformers/paraphrase-multilingual-mpnet-base-v2`](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) as a standard `AutoModelForSequenceClassification` (no custom code).

## Usage

```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer

repo = "Badalt/intent-classifier-mpnet"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForSequenceClassification.from_pretrained(repo).eval()

texts = ["whats the eta on LD-55501 pls", "¿Cuántos remolques hay en el patio?"]
with torch.no_grad():
    logits = model(**tok(texts, padding=True, truncation=True, max_length=64, return_tensors="pt")).logits
probs = torch.softmax(logits / model.config.calibration_temperature, dim=-1)
conf, idx = probs.max(-1)
for c, i in zip(conf, idx):
    label = "unknown" if c < model.config.unknown_threshold else model.config.id2label[int(i)]
    print(label, round(float(c), 3))
```

Reject rule: `unknown` if `max softmax(logits / 0.4622) < 0.5735`. The threshold keeps 95.0% of known-intent validation messages; the temperature was fitted on the same validation set.

## Labels

`ai_agent_performance`, `appointment_manager`, `chitchat`, `customer_support`, `document_processing`, `knowledge_base`, `orders`, `other`, `shipment_information.analytics`, `shipment_information.disruptions`, `shipment_information.realtime_query`, `yard_management` (+ `unknown` via the reject rule).

## Training data

500 labelled synthetic chat messages (12 intents, 30–55 per class; ~13% non-English: ES, FR, DE, ZH, NL, AR, IT; noisy, colloquial). The dataset is confidential and **not** distributed with this model. Split: stratified 70/15/15 (347 / 76 / 77) with near-duplicate paraphrases kept in the same split; seed 42.

## Training procedure

lr 5e-05, batch 16, ≤15 epochs with early stopping on validation macro-F1 (best epoch 4), linear decay, warmup 0.1, weight decay 0.01, class-weighted cross-entropy, max_length 64, seed 42. Backbone and learning rate chosen by 5-fold cross-validation among 3 multilingual encoders × 2 learning rates.

## Evaluation

**Held-out test set (n=77, never used for any decision):** macro-F1 **0.885** (95% bootstrap CI 0.791–0.950), accuracy 0.896; English 0.879 vs non-English 1.000 accuracy (n=11).

**5-fold CV on train+val (n=423, out-of-fold):** macro-F1 0.920 (fold mean 0.920 ± 0.028); non-English accuracy 0.942.

| intent | precision | recall | F1 | n |
|---|---|---|---|---|
| `ai_agent_performance` | 1.00 | 1.00 | 1.00 | 6 |
| `appointment_manager` | 0.88 | 1.00 | 0.93 | 7 |
| `chitchat` | 0.83 | 1.00 | 0.91 | 5 |
| `customer_support` | 0.86 | 1.00 | 0.92 | 6 |
| `document_processing` | 1.00 | 0.83 | 0.91 | 6 |
| `knowledge_base` | 0.67 | 1.00 | 0.80 | 6 |
| `orders` | 1.00 | 0.86 | 0.92 | 7 |
| `other` | 1.00 | 0.40 | 0.57 | 5 |
| `shipment_information.analytics` | 1.00 | 0.80 | 0.89 | 10 |
| `shipment_information.disruptions` | 0.83 | 1.00 | 0.91 | 5 |
| `shipment_information.realtime_query` | 0.89 | 1.00 | 0.94 | 8 |
| `yard_management` | 1.00 | 0.83 | 0.91 | 6 |

**Open-set (unknown intents).** A twin model trained without `yard_management` and `document_processing` was tested on known messages plus all messages of those unseen intents: max-softmax reject rule AUROC 0.791, rejection recall 26.2% at 93.8% retention; best scorer (kNN k=1 [mean]) AUROC 0.821.

## Intended use & limitations

- First-step router for a logistics assistant; low-confidence messages should go to a clarification / fallback path.
- Trained on 500 synthetic rows: expect lower accuracy on real traffic; monitor confidence, unknown-rate and language mix for drift and re-calibrate the threshold on fresh labelled data.
- The three `shipment_information.*` intents are semantically close and account for a large share of errors.
- Non-English evaluation rests on few examples; languages not seen in training (e.g. Arabic, Italian) are untested beyond anecdotes.
- `other` / `chitchat` are trained catch-alls; genuinely new intents are handled only by the confidence threshold.

Training curves and metrics: https://wandb.ai/badalthakur2212-iisc/intent-classifier/runs/ffy90jmz