Push model using huggingface_hub.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 384,
+  "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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+---
+library_name: setfit
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+metrics:
+- accuracy
+widget:
+- text: parking aeroport charles de gaulle carte
+- text: achat académie dressage canin carte
+- text: prlv sepa agence immobiliere commission vente
+- text: facture carte toilettage beautydog nice carte
+- text: facture carte du adobe creative cloud photo carte
+pipeline_tag: text-classification
+inference: true
+model-index:
+- name: SetFit
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.2727272727272727
+      name: Accuracy
+---
+
+# SetFit
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. 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:** [Unknown](https://huggingface.co/unknown) -->
+- **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:** 44 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                                                                                                                                        |
+|:-------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------|
+| Shopping / electronics & multimedia        | <ul><li>'paiement darty merignac carte'</li><li>'payement apple store carte carte usa usd commission'</li></ul>                                 |
+| Other / kids                               | <ul><li>'debit carte jeuxvideokidz com carte'</li><li>'achat carte magic cake anniversaire theo carte'</li></ul>                                |
+| Bank services / other                      | <ul><li>'paiement frais opposition cheque carte'</li><li>'paiement frais demande rib iban supplémentaires carte'</li></ul>                      |
+| Housing / rent                             | <ul><li>'prlv sepa studio centre ville lyon carte'</li><li>'prelevement loyer residence les cerisiers carte'</li></ul>                          |
+| Transportation / other                     | <ul><li>'service assistance dépannage routier carte'</li><li>'parking aeroport charles de gaulle carte'</li></ul>                               |
+| Bank services / transfers                  | <ul><li>'virement pour participation voyage scolaire sarah carte'</li><li>'virement sortant vers elodie dupont carte'</li></ul>                 |
+| Investment / retirement & savings          | <ul><li>'virement pee plan épargne entreprise carte'</li><li>'cotisation assurance vie caisse d epargne carte'</li></ul>                        |
+| Other / taxes                              | <ul><li>'prelevement automatique taxe d amenagement'</li><li>'facture taxe sur les ordures menageres'</li></ul>                                 |
+| Healthy & Beauty / other                   | <ul><li>'abonnement trimestre club danse rythmo'</li><li>'cotisation annuelle association bien être soi'</li></ul>                              |
+| Investment / securities                    | <ul><li>'investissement etf cac carte'</li><li>'transaction actions netflix carte usd'</li></ul>                                                |
+| Housing / other                            | <ul><li>'prlv sepa du alarmes securitas direct'</li><li>'facture carte du leroy merlin montigny carte'</li></ul>                                |
+| Housing / house loan                       | <ul><li>'prlv credit immobilier ing direct echeance num'</li><li>'virement recu mensualite pret logis credit agricoche du'</li></ul>            |
+| Housing / utilities & bills                | <ul><li>'prlv sepa orange france telecom'</li><li>'prlv sepa eau de paris'</li></ul>                                                            |
+| Bank services / general fees               | <ul><li>'frais operation non europeenne carte'</li><li>'frais renouvellement carte bancaire'</li></ul>                                          |
+| Leisure & Entertainment / culture & events | <ul><li>'prlv sepa cinema cgr lille'</li><li>'achat carte billet expo universselle carte'</li></ul>                                             |
+| Transportation / taxi & carpool            | <ul><li>'facture carte du didi chengdu carte chn cny commission'</li><li>'facture carte du kakao taxi seoul carte kor krw commission'</li></ul> |
+| Shopping / other                           | <ul><li>'achat arts et decoration bleneau carte'</li><li>'facture carte du boutique des artistes lyon carte'</li></ul>                          |
+| Recurrent Payments / loans                 | <ul><li>'prlv recurrent banque postale pret perso carte'</li><li>'debit recurrent cic pret etudiant pretcicunive'</li></ul>                     |
+| Healthy & Beauty / doctor fees             | <ul><li>'prlv sepa centre medical les lilas frzzz cde wefr'</li><li>'prlv sepa centre chirurgical val d or frzzz cdc foeer'</li></ul>           |
+| Bank services / withdrawal                 | <ul><li>'retrait dab ecobanque lyon carte fr'</li><li>'retrait dab banqcentral montpellier carte fr'</li></ul>                                  |
+| Other / other                              | <ul><li>'paiement cotisation club d escalade les rocs'</li><li>'paiement en ligne site de don leucan'</li></ul>                                 |
+| Healthy & Beauty / pharmacy                | <ul><li>'facture du pharmacie soleil levant carte'</li><li>'facture carte du pharmacie bellerose carte'</li></ul>                               |
+| Transportation / fuel                      | <ul><li>'debit station petronas nice carte'</li><li>'transac carte du oil berlin carte ger'</li></ul>                                           |
+| Shopping / sporting goods                  | <ul><li>'facture carte patagonia grenoble carte'</li><li>'debit adidas running store nice carte'</li></ul>                                      |
+| Food & Drinks / groceries                  | <ul><li>'debit chocolaterie dulce carte'</li><li>'prlv sepa epicerie du sud carte'</li></ul>                                                    |
+| Other / pets                               | <ul><li>'achat académie dressage canin carte'</li><li>'facture carte toilettage beautydog nice carte'</li></ul>                                 |
+| Investment / real estate                   | <ul><li>'loyers reçus locataire paris eme carte'</li><li>'prlv sepa agence immobiliere commission vente'</li></ul>                              |
+| Shopping / clothing                        | <ul><li>'paiement carte du gucci opera paris carte'</li><li>'achat carte adidas originals store carte deu'</li></ul>                            |
+| Shopping / housing equipment               | <ul><li>'paiement par carte brico depot nice carte'</li><li>'achat chez tool co toulouse carte'</li></ul>                                       |
+| Transportation / maitenance                | <ul><li>'facture carte du garage bonvolant poitiers carte'</li><li>'paiement carte du garage rénov clim reims carte'</li></ul>                  |
+| Recurrent Payments / other                 | <ul><li>'prlv sepa soutien scolaire en ligne mathplus'</li><li>'prlv sepa club sportif maxiforme'</li></ul>                                     |
+| Recurrent Payments / insurance             | <ul><li>'prlv sepa assurance emprunteur bnp paribas'</li><li>'prlv sepa assurance habitation axa'</li></ul>                                     |
+| Healthy & Beauty / veterinary              | <ul><li>'debit soin du veto express marseille carte'</li><li>'vaccins chat clinique du parc toulouse carte'</li></ul>                           |
+| Transportation / public transportation     | <ul><li>'pass ferry corsica corsica linea carte'</li><li>'recharge navigo semaine ratp carte'</li></ul>                                         |
+| Healthy & Beauty / beauty & self-care      | <ul><li>'facture carte du institut beaute pure carte'</li><li>'facture carte du coiffeur coupe chic carte'</li></ul>                            |
+| Leisure & Entertainment / other            | <ul><li>'abonnement mensuel canal carte'</li><li>'facture carte du spotify premium carte usa'</li></ul>                                         |
+| Food & Drinks / eating out                 | <ul><li>'facture carte du chez laurette lyon carte'</li><li>'facture carte du le gourmet vegan carte'</li></ul>                                 |
+| Housing / services & maintenance           | <ul><li>'prlv sepa renovaction'</li><li>'facture carte du nettoyage professionnel sarl carte'</li></ul>                                         |
+| Leisure & Entertainment / travel           | <ul><li>'facture carte du air france carte'</li><li>'virement sortant vacation savings for maldives frzzz date'</li></ul>                       |
+| Leisure & Entertainment / sports & hobbies | <ul><li>'paiement en ligne du go sport paris carte'</li><li>'paiement en ligne du strava subscription carte usd'</li></ul>                      |
+| Investment / other                         | <ul><li>'achat actions ia revolution carte'</li><li>'participation crowdfunding waterclean projet'</li></ul>                                    |
+| Transportation / car loan & leasing        | <ul><li>'prelevement sepa creditauto favorisxcb carte'</li><li>'paiement mensualite volkswagen polo v loc vwpolo'</li></ul>                     |
+| Recurrent Payments / subscription          | <ul><li>'abonnement vpnsecure net carte'</li><li>'facture carte du adobe creative cloud photo carte'</li></ul>                                  |
+| Food & Drinks / other                      | <ul><li>'payment gourmet popcorn shop carte'</li><li>'achat du confiserie pierre carte'</li></ul>                                               |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.2727   |
+
+## 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("HEN10/setfit-particular-transaction-solon-embeddings-labels-large-kaggle-automatisation-v1")
+# Run inference
+preds = model("achat académie dressage canin carte")
+```
+
+<!--
+### 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   | 3   | 6.2045 | 10  |
+
+| Label                                      | Training Sample Count |
+|:-------------------------------------------|:----------------------|
+| Housing / rent                             | 2                     |
+| Housing / house loan                       | 2                     |
+| Housing / utilities & bills                | 2                     |
+| Housing / services & maintenance           | 2                     |
+| Housing / other                            | 2                     |
+| Food & Drinks / groceries                  | 2                     |
+| Food & Drinks / eating out                 | 2                     |
+| Food & Drinks / other                      | 2                     |
+| Leisure & Entertainment / sports & hobbies | 2                     |
+| Leisure & Entertainment / culture & events | 2                     |
+| Leisure & Entertainment / travel           | 2                     |
+| Leisure & Entertainment / other            | 2                     |
+| Transportation / car loan & leasing        | 2                     |
+| Transportation / fuel                      | 2                     |
+| Transportation / public transportation     | 2                     |
+| Transportation / taxi & carpool            | 2                     |
+| Transportation / maitenance                | 2                     |
+| Transportation / other                     | 2                     |
+| Recurrent Payments / loans                 | 2                     |
+| Recurrent Payments / insurance             | 2                     |
+| Recurrent Payments / subscription          | 2                     |
+| Recurrent Payments / other                 | 2                     |
+| Investment / securities                    | 2                     |
+| Investment / retirement & savings          | 2                     |
+| Investment / real estate                   | 2                     |
+| Investment / other                         | 2                     |
+| Shopping / clothing                        | 2                     |
+| Shopping / electronics & multimedia        | 2                     |
+| Shopping / sporting goods                  | 2                     |
+| Shopping / housing equipment               | 2                     |
+| Shopping / other                           | 2                     |
+| Healthy & Beauty / doctor fees             | 2                     |
+| Healthy & Beauty / pharmacy                | 2                     |
+| Healthy & Beauty / beauty & self-care      | 2                     |
+| Healthy & Beauty / veterinary              | 2                     |
+| Healthy & Beauty / other                   | 2                     |
+| Bank services / transfers                  | 2                     |
+| Bank services / withdrawal                 | 2                     |
+| Bank services / general fees               | 2                     |
+| Bank services / other                      | 2                     |
+| Other / taxes                              | 2                     |
+| Other / kids                               | 2                     |
+| Other / pets                               | 2                     |
+| Other / other                              | 2                     |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- num_epochs: (1, 1)
+- max_steps: -1
+- sampling_strategy: oversampling
+- body_learning_rate: (2e-05, 1e-05)
+- head_learning_rate: 0.01
+- loss: CosineSimilarityLoss
+- distance_metric: cosine_distance
+- margin: 0.25
+- end_to_end: True
+- use_amp: False
+- warmup_proportion: 0.1
+- seed: 6
+- eval_max_steps: -1
+- load_best_model_at_end: False
+
+### Training Results
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0021 | 1    | 0.1771        | -               |
+| 0.1057 | 50   | 0.1325        | -               |
+| 0.2114 | 100  | 0.1132        | -               |
+| 0.3171 | 150  | 0.0424        | -               |
+| 0.4228 | 200  | 0.0329        | -               |
+| 0.5285 | 250  | 0.0581        | -               |
+| 0.6342 | 300  | 0.0155        | -               |
+| 0.7400 | 350  | 0.0157        | -               |
+| 0.8457 | 400  | 0.0138        | -               |
+| 0.9514 | 450  | 0.0237        | -               |
+
+### Framework Versions
+- Python: 3.10.13
+- SetFit: 1.0.3
+- Sentence Transformers: 2.6.1
+- Transformers: 4.39.3
+- PyTorch: 2.1.2
+- Datasets: 2.17.0
+- Tokenizers: 0.15.2
+
+## 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": "sentence-transformers/all-MiniLM-L12-v2",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.39.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "2.0.0",
+    "transformers": "4.6.1",
+    "pytorch": "1.8.1"
+  },
+  "prompts": {},
+  "default_prompt_name": null
+}

config_setfit.json

@@ -0,0 +1,49 @@
+{
+  "labels": [
+    "Housing / rent",
+    "Housing / house loan",
+    "Housing / utilities & bills",
+    "Housing / services & maintenance",
+    "Housing / other",
+    "Food & Drinks / groceries",
+    "Food & Drinks / eating out",
+    "Food & Drinks / other",
+    "Leisure & Entertainment / sports & hobbies",
+    "Leisure & Entertainment / culture & events",
+    "Leisure & Entertainment / travel",
+    "Leisure & Entertainment / other",
+    "Transportation / car loan & leasing",
+    "Transportation / fuel",
+    "Transportation / public transportation",
+    "Transportation / taxi & carpool",
+    "Transportation / maitenance",
+    "Transportation / other",
+    "Recurrent Payments / loans",
+    "Recurrent Payments / insurance",
+    "Recurrent Payments / subscription",
+    "Recurrent Payments / other",
+    "Investment / securities",
+    "Investment / retirement & savings",
+    "Investment / real estate",
+    "Investment / other",
+    "Shopping / clothing",
+    "Shopping / electronics & multimedia",
+    "Shopping / sporting goods",
+    "Shopping / housing equipment",
+    "Shopping / other",
+    "Healthy & Beauty / doctor fees",
+    "Healthy & Beauty / pharmacy",
+    "Healthy & Beauty / beauty & self-care",
+    "Healthy & Beauty / veterinary",
+    "Healthy & Beauty / other",
+    "Bank services / transfers",
+    "Bank services / withdrawal",
+    "Bank services / general fees",
+    "Bank services / other",
+    "Other / taxes",
+    "Other / kids",
+    "Other / pets",
+    "Other / other"
+  ],
+  "normalize_embeddings": false
+}

model.safetensors

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model_head.pkl

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modules.json

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+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
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+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

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

special_tokens_map.json

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+    "lstrip": false,
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+      "content": "[PAD]",
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+  "do_lower_case": true,
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+  "stride": 0,
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+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}