Instructions to use FlowRank/mailSort with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FlowRank/mailSort with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="FlowRank/mailSort")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("FlowRank/mailSort", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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feat: charge labeled_emails@main par défaut et finalise model card v2
- Ajoute mailsort.dataset (révision main = dernière version) - eval utilise FlowRank/mailSort/model par défaut - README model card HF (20 labels, accuracy 96%) Co-authored-by: Hilarion Lefuneste <hilarionlefuneste@tutamail.com> Co-authored-by: Cursor <cursoragent@cursor.com>36a680b -
feat(model): publie le modèle v2 (20 labels, 96% test acc)
Artefacts entraînés sur FlowRank/labeled_emails@enrichment. Mise à jour README (versioning v1/v2). Co-authored-by: Hilarion Lefuneste <hilarionlefuneste@tutamail.com> Co-authored-by: Cursor <cursoragent@cursor.com>300ec53 -
feat(training): support dataset revision for enrichment branch
Ajoute --dataset-revision à train et eval pour charger FlowRank/labeled_emails@enrichment (20 labels). Co-authored-by: Hilarion Lefuneste <hilarionlefuneste@tutamail.com> Co-authored-by: Cursor <cursoragent@cursor.com>8d69eed
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chore: test ssh signing
Unverified signatureCo-authored-by: Cursor <cursoragent@cursor.com>be4db4a -
feat(training): pipeline minimal train/test + artefacts HF
Unverified signature- Entraîne un classifieur à partir de FlowRank/labeled_emails - Ajoute scripts d’évaluation et de préparation des artefacts sous model/ - Documente l’usage (train/test/publish) dans README Co-authored-by: Hilarion Lefuneste <hilarionlefuneste@tutamail.com> Co-authored-by: Cursor <cursoragent@cursor.com>8153a62
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initial commit
Unverified signature60c9bc5