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library_name: transformers
pipeline_tag: text-classification
tags:
- email
- text-classification
language:
- en
datasets:
- FlowRank/labeled_emails
base_model: distilbert-base-uncased
license: mit
metrics:
- accuracy
model-index:
- name: mailSort
results:
- task:
type: text-classification
dataset:
name: FlowRank/labeled_emails
type: FlowRank/labeled_emails
metrics:
- type: accuracy
value: 0.9601
widget:
- text: "Subject: Insurance claim\n\nBody: Hello, I need to update my policy urgently before the deadline."
---
## mailSort
Classifieur d’e-mails **20 classes** (catégorie + urgence), basé sur DistilBERT.
Entraîné sur [`FlowRank/labeled_emails`](https://huggingface.co/datasets/FlowRank/labeled_emails) (révision `main`, ~19,8k exemples).
**Accuracy test : 96,01 %** (1 900 / 1 979).
| Version | Labels | Révision dataset | Révision modèle |
|---------|--------|------------------|-----------------|
| **v2** (courante) | 20 | `main` ou `v2.0` | `main` ou `v2.0` |
| v1 | 10 | `v1.0` | `v1.0` |
### Labels (v2)
Chaque catégorie est déclinée en `_urgent` / `_not_urgent` : `family`, `finance`, `games`, `human resources`, `medical`, `pets`, `school`, `software engineering`, `sport`, `work/airbus`.
## Inférence
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
tok = AutoTokenizer.from_pretrained("FlowRank/mailSort", subfolder="model")
model = AutoModelForSequenceClassification.from_pretrained("FlowRank/mailSort", subfolder="model")
clf = pipeline("text-classification", model=model, tokenizer=tok, truncation=True)
text = "Subject: Insurance claim\n\nBody: I need to update my policy urgently."
print(clf(text))
```
## Développement local
Repo Python minimal pour entraîner, évaluer et publier. Le script principal est `mailsort.train`.
Par défaut, le code charge la **dernière version du dataset** (`FlowRank/labeled_emails@main`).
### Installation
```bash
uv sync
```
### Entraîner + évaluer
```bash
uv run python -m mailsort.train \
--model-name distilbert-base-uncased \
--hub-model-id FlowRank/mailSort \
--num-train-epochs 2
```
Pour épingler une version précise du dataset :
```bash
uv run python -m mailsort.train --dataset-revision v1.0
```
### Évaluer le modèle publié
```bash
uv run python -m mailsort.eval --split test
```
Évalue par défaut `FlowRank/mailSort` (sous-dossier `model/`) sur `FlowRank/labeled_emails@main`.
### Publier via Git (README + `model/`)
```bash
uv run python -m mailsort.prepare_model --outputs-dir outputs --model-dir model
git add README.md model
git commit -m "chore(model): update published artifacts"
git push
```
Le push Hub automatique via `Trainer` nécessite `HF_TOKEN` :
```bash
export HF_TOKEN="..."
uv run python -m mailsort.train --hub-model-id FlowRank/mailSort
```
|