---
language:
- fr
license: mit
library_name: onnx
pipeline_tag: text-classification
tags:
- intent-classification
- sentence-embeddings
- onnx
- french
base_model: rolf-mozilla/french-embedding-e5-distilled
datasets:
- Mozilla/query-intent-detection-dataset-french
---

# French query intent (Chat vs Search) + embedding in one pass — q8 ONNX

[`rolf-mozilla/french-embedding-e5-distilled`](https://huggingface.co/rolf-mozilla/french-embedding-e5-distilled)
with a linear **Chat / Search** intent head on its 256-d embedding, exported as **one
graph with three outputs**, so a single forward pass gives both the intent and the
embedding — no separate classifier model. **28.3 MB** int8 ONNX.

| output | shape | meaning |
|---|---|---|
| `logits` | [batch, 2] | intent logits, `0 = Chat`, `1 = Search` |
| `sentence_embedding` | [batch, 256] | mean-pooled, L2-normalized embedding (same as the embedding repo, cos ≥ 0.997) |
| `last_hidden_state` | [batch, seq, 256] | per-token vectors (mean-pool these for the embedding yourself) |

## Results (French test split of Mozilla/query-intent-detection-dataset-french, q8)

| model | extra cost on top of the embedding model | test acc | macro-F1 | test acc (queries unseen in train) |
|---|---|---|---|---|
| **this model** | **514 params (linear head)** | **0.748** | **0.738** | **0.727** |
| Mozilla/query-intent-detection-en-fr | separate 14 MB model + forward pass | 0.713 | 0.710 | 0.696 |
| multilingual-e5-small, fully fine-tuned | separate 118 MB model | 0.713 | 0.697 | 0.700 |
| all-MiniLM-L6-v2, fully fine-tuned | separate 23 MB model | 0.706 | 0.689 | 0.691 |

The test split (1,378 queries) is ~±2.3 accuracy points at 95% confidence. Train and
validation are synthetic; validation accuracy (~0.96 here) does not predict test accuracy —
full fine-tuning, LoRA, MLP heads and joint embedding+intent training all scored higher on
validation but *lower* on test than this frozen-embedding linear head.

## Usage (Python, onnxruntime)
```python
import numpy as np, onnxruntime as ort
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer

repo = "rolf-mozilla/french-intent-embedding-e5-distilled"
tok = AutoTokenizer.from_pretrained(repo)
sess = ort.InferenceSession(hf_hub_download(repo, "onnx/model_quantized.onnx"))

e = tok(["météo demain à Lyon", "écris un poème sur l'automne"], padding=True,
        truncation=True, max_length=64, return_tensors="np")
logits, emb = sess.run(["logits", "sentence_embedding"],
                       {"input_ids": e["input_ids"], "attention_mask": e["attention_mask"]})
labels = np.array(["Chat", "Search"])[logits.argmax(-1)]
```

## Training

- Embedding model frozen (see its card for how it was distilled and the data/licensing notes,
  including the MS MARCO lineage caveat of its `multilingual-e5-small` base).
- Intent head: logistic regression on the normalized q8 embedding, trained on the 233k-query
  train split of `Mozilla/query-intent-detection-dataset-french`; feature standardization is
  folded into the head's weights.
- French only; queries truncated to 64 tokens for intent.