Push model using huggingface_hub.

1_Pooling/config.json

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+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

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+---
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: Trouver un vol de Marseille à Berlin, merci.
+- text: La Corée du Nord a-t-elle des alertes de voyage que je devrais être conscientes
+- text: Combien de côtés dans un hexagone
+- text: Quelles sont les alertes pour le Mexique en ce moment ?
+- text: Des alertes de voyage en France sont-elles en vigueur ?
+metrics:
+- accuracy
+pipeline_tag: text-classification
+library_name: setfit
+inference: true
+base_model: dangvantuan/sentence-camembert-base
+---
+
+# SetFit with dangvantuan/sentence-camembert-base
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [dangvantuan/sentence-camembert-base](https://huggingface.co/dangvantuan/sentence-camembert-base) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
+
+The model has been trained using an efficient few-shot learning technique that involves:
+
+1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
+2. Training a classification head with features from the fine-tuned Sentence Transformer.
+
+## Model Details
+
+### Model Description
+- **Model Type:** SetFit
+- **Sentence Transformer body:** [dangvantuan/sentence-camembert-base](https://huggingface.co/dangvantuan/sentence-camembert-base)
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **Maximum Sequence Length:** 128 tokens
+- **Number of Classes:** 9 classes
+<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
+- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
+- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
+
+### Model Labels
+| Label | Examples                                                                                                                                                                                                                           |
+|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 0     | <ul><li>"S'il vous plaît, aidez-moi à réserver un endroit où séjourner à Pittsburgh du lundi au vendredi"</li><li>'Réservez-moi une chambre du 11 au 15 novembre à Cali'</li><li>'Je souhaite réserver un hôtel à Rome.'</li></ul> |
+| 1     | <ul><li>'Trouvez-moi des vols aller-retour de LAX vers SFOX'</li><li>'Réservez un vol de Chicago à DC lundi et revenant mercredi'</li><li>'Réserver un vol pour New York, aller-retour.'</li></ul>                                 |
+| 2     | <ul><li>'Y a-t-il des alertes de voyage pour la Syrie'</li><li>'La Corée du Nord a-t-elle des alertes de voyage que je devrais être conscientes'</li><li>'Y a-t-il des alertes de voyage pour le Japon ?'</li></ul>                |
+| 8     | <ul><li>'Combien de côtés dans un hexagone'</li><li>'Quand Nintendo a-t-il été créé'</li><li>"Quelle taille d'essuie-glace cette voiture prend-t-elle"</li></ul>                                                                   |
+| 3     | <ul><li>'Y a-t-il une limite de poids de bagage cabine'</li><li>'Quelles sont les règles concernant les bagages à main pour les vols Virgin Airlines'</li><li>'Quelles sont les règles pour les bagages à main?'</li></ul>         |
+| 4     | <ul><li>'Comment dire «hôtel» en finnois'</li><li>"Si j'étais japonais, comment dirais-je que je suis touriste"</li><li>"Pouvez-vous traduire 'bonjour' en espagnol?"</li></ul>                                                    |
+| 5     | <ul><li>'Savez-vous qui je devrais contacter car mes valises ne sont pas arrivées'</li><li>'Je pense que mes bagages ont été perdus'</li><li>"J'ai perdu mes bagages, que dois-je faire?"</li></ul>                                |
+| 6     | <ul><li>"quelle est la porte d'embarquement pour mon vol AF847"</li><li>'Quand mon vol décollera-t-il'</li><li>'Où puis-je vérifier le statut de mon vol?'</li></ul>                                                               |
+| 7     | <ul><li>'Y a-t-il des activités touristiques amusantes en Australie'</li><li>'Donnez-moi une liste de choses à faire à Orlando'</li><li>'Pouvez-vous me suggérer des destinations de voyage?'</li></ul>                            |
+
+## Uses
+
+### Direct Use for Inference
+
+First install the SetFit library:
+
+```bash
+pip install setfit
+```
+
+Then you can load this model and run inference.
+
+```python
+from setfit import SetFitModel
+
+# Download from the 🤗 Hub
+model = SetFitModel.from_pretrained("leaboussekeyt/setfit_model")
+# Run inference
+preds = model("Combien de côtés dans un hexagone")
+```
+
+<!--
+### Downstream Use
+
+*List how someone could finetune this model on their own dataset.*
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Set Metrics
+| Training set | Min | Median | Max |
+|:-------------|:----|:-------|:----|
+| Word count   | 4   | 8.4302 | 16  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 10                    |
+| 1     | 10                    |
+| 2     | 10                    |
+| 3     | 10                    |
+| 4     | 10                    |
+| 5     | 10                    |
+| 6     | 10                    |
+| 7     | 10                    |
+| 8     | 6                     |
+
+### Training Hyperparameters
+- batch_size: (32, 32)
+- num_epochs: (2, 2)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 20
+- body_learning_rate: (2e-05, 1e-05)
+- head_learning_rate: 0.01
+- loss: CosineSimilarityLoss
+- distance_metric: cosine_distance
+- margin: 0.25
+- end_to_end: False
+- use_amp: False
+- warmup_proportion: 0.1
+- l2_weight: 0.01
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: False
+
+### Training Results
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0093 | 1    | 0.1927        | -               |
+| 0.4630 | 50   | 0.0828        | -               |
+| 0.9259 | 100  | 0.0139        | -               |
+| 1.3889 | 150  | 0.0055        | -               |
+| 1.8519 | 200  | 0.0036        | -               |
+
+### Framework Versions
+- Python: 3.11.11
+- SetFit: 1.1.1
+- Sentence Transformers: 3.4.1
+- Transformers: 4.49.0
+- PyTorch: 2.6.0+cu124
+- Datasets: 3.4.1
+- Tokenizers: 0.21.1
+
+## Citation
+
+### BibTeX
+```bibtex
+@article{https://doi.org/10.48550/arxiv.2209.11055,
+    doi = {10.48550/ARXIV.2209.11055},
+    url = {https://arxiv.org/abs/2209.11055},
+    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
+    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
+    title = {Efficient Few-Shot Learning Without Prompts},
+    publisher = {arXiv},
+    year = {2022},
+    copyright = {Creative Commons Attribution 4.0 International}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

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+{
+  "_name_or_path": "dangvantuan/sentence-camembert-base",
+  "architectures": [
+    "CamembertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "eos_token_ids": 0,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "camembert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.49.0",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 32005
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.4.1",
+    "transformers": "4.49.0",
+    "pytorch": "2.6.0+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

config_setfit.json

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+{
+  "labels": null,
+  "normalize_embeddings": false
+}

model.safetensors

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model_head.pkl

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modules.json

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+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
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+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

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+{
+  "max_seq_length": 128,
+  "do_lower_case": false
+}

special_tokens_map.json

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tokenizer_config.json

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+  "tokenizer_class": "CamembertTokenizer",
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+  "truncation_strategy": "longest_first",
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+}