HEN10 commited on
Commit
eaa7284
1 Parent(s): dd3075d

Push model using huggingface_hub.

Browse files
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README.md ADDED
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+ ---
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+ library_name: setfit
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+ tags:
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+ - setfit
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+ - sentence-transformers
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+ - text-classification
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+ - generated_from_setfit_trainer
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+ metrics:
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+ - accuracy
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+ widget:
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+ - text: parking aeroport charles de gaulle carte
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+ - text: achat académie dressage canin carte
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+ - text: prlv sepa agence immobiliere commission vente
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+ - text: facture carte toilettage beautydog nice carte
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+ - text: facture carte du adobe creative cloud photo carte
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+ pipeline_tag: text-classification
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+ inference: true
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+ model-index:
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+ - name: SetFit
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+ results:
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+ - task:
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+ type: text-classification
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+ name: Text Classification
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+ dataset:
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+ name: Unknown
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+ type: unknown
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+ split: test
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+ metrics:
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+ - type: accuracy
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+ value: 0.2727272727272727
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+ name: Accuracy
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+ ---
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+
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+ # SetFit
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+
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+ 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.
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+
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+ The model has been trained using an efficient few-shot learning technique that involves:
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+
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+ 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
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+ 2. Training a classification head with features from the fine-tuned Sentence Transformer.
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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** SetFit
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+ <!-- - **Sentence Transformer:** [Unknown](https://huggingface.co/unknown) -->
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+ - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
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+ - **Maximum Sequence Length:** 128 tokens
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+ - **Number of Classes:** 44 classes
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+ <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
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+ <!-- - **Language:** Unknown -->
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+ <!-- - **License:** Unknown -->
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+
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+ ### Model Sources
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+
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+ - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
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+ - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
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+ - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
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+
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+ ### Model Labels
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+ | Label | Examples |
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+ |:-------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------|
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+ | Shopping / electronics & multimedia | <ul><li>'paiement darty merignac carte'</li><li>'payement apple store carte carte usa usd commission'</li></ul> |
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+ | Other / kids | <ul><li>'debit carte jeuxvideokidz com carte'</li><li>'achat carte magic cake anniversaire theo carte'</li></ul> |
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+ | Bank services / other | <ul><li>'paiement frais opposition cheque carte'</li><li>'paiement frais demande rib iban supplémentaires carte'</li></ul> |
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+ | Housing / rent | <ul><li>'prlv sepa studio centre ville lyon carte'</li><li>'prelevement loyer residence les cerisiers carte'</li></ul> |
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+ | Transportation / other | <ul><li>'service assistance dépannage routier carte'</li><li>'parking aeroport charles de gaulle carte'</li></ul> |
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+ | Bank services / transfers | <ul><li>'virement pour participation voyage scolaire sarah carte'</li><li>'virement sortant vers elodie dupont carte'</li></ul> |
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+ | Investment / retirement & savings | <ul><li>'virement pee plan épargne entreprise carte'</li><li>'cotisation assurance vie caisse d epargne carte'</li></ul> |
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+ | Other / taxes | <ul><li>'prelevement automatique taxe d amenagement'</li><li>'facture taxe sur les ordures menageres'</li></ul> |
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+ | Healthy & Beauty / other | <ul><li>'abonnement trimestre club danse rythmo'</li><li>'cotisation annuelle association bien être soi'</li></ul> |
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+ | Investment / securities | <ul><li>'investissement etf cac carte'</li><li>'transaction actions netflix carte usd'</li></ul> |
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+ | Housing / other | <ul><li>'prlv sepa du alarmes securitas direct'</li><li>'facture carte du leroy merlin montigny carte'</li></ul> |
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+ | Housing / house loan | <ul><li>'prlv credit immobilier ing direct echeance num'</li><li>'virement recu mensualite pret logis credit agricoche du'</li></ul> |
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+ | Housing / utilities & bills | <ul><li>'prlv sepa orange france telecom'</li><li>'prlv sepa eau de paris'</li></ul> |
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+ | Bank services / general fees | <ul><li>'frais operation non europeenne carte'</li><li>'frais renouvellement carte bancaire'</li></ul> |
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+ | Leisure & Entertainment / culture & events | <ul><li>'prlv sepa cinema cgr lille'</li><li>'achat carte billet expo universselle carte'</li></ul> |
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+ | 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> |
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+ | Shopping / other | <ul><li>'achat arts et decoration bleneau carte'</li><li>'facture carte du boutique des artistes lyon carte'</li></ul> |
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+ | Recurrent Payments / loans | <ul><li>'prlv recurrent banque postale pret perso carte'</li><li>'debit recurrent cic pret etudiant pretcicunive'</li></ul> |
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+ | 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> |
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+ | Bank services / withdrawal | <ul><li>'retrait dab ecobanque lyon carte fr'</li><li>'retrait dab banqcentral montpellier carte fr'</li></ul> |
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+ | Other / other | <ul><li>'paiement cotisation club d escalade les rocs'</li><li>'paiement en ligne site de don leucan'</li></ul> |
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+ | Healthy & Beauty / pharmacy | <ul><li>'facture du pharmacie soleil levant carte'</li><li>'facture carte du pharmacie bellerose carte'</li></ul> |
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+ | Transportation / fuel | <ul><li>'debit station petronas nice carte'</li><li>'transac carte du oil berlin carte ger'</li></ul> |
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+ | Shopping / sporting goods | <ul><li>'facture carte patagonia grenoble carte'</li><li>'debit adidas running store nice carte'</li></ul> |
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+ | Food & Drinks / groceries | <ul><li>'debit chocolaterie dulce carte'</li><li>'prlv sepa epicerie du sud carte'</li></ul> |
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+ | Other / pets | <ul><li>'achat académie dressage canin carte'</li><li>'facture carte toilettage beautydog nice carte'</li></ul> |
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+ | Investment / real estate | <ul><li>'loyers reçus locataire paris eme carte'</li><li>'prlv sepa agence immobiliere commission vente'</li></ul> |
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+ | Shopping / clothing | <ul><li>'paiement carte du gucci opera paris carte'</li><li>'achat carte adidas originals store carte deu'</li></ul> |
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+ | Shopping / housing equipment | <ul><li>'paiement par carte brico depot nice carte'</li><li>'achat chez tool co toulouse carte'</li></ul> |
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+ | Transportation / maitenance | <ul><li>'facture carte du garage bonvolant poitiers carte'</li><li>'paiement carte du garage rénov clim reims carte'</li></ul> |
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+ | Recurrent Payments / other | <ul><li>'prlv sepa soutien scolaire en ligne mathplus'</li><li>'prlv sepa club sportif maxiforme'</li></ul> |
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+ | Recurrent Payments / insurance | <ul><li>'prlv sepa assurance emprunteur bnp paribas'</li><li>'prlv sepa assurance habitation axa'</li></ul> |
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+ | Healthy & Beauty / veterinary | <ul><li>'debit soin du veto express marseille carte'</li><li>'vaccins chat clinique du parc toulouse carte'</li></ul> |
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+ | Transportation / public transportation | <ul><li>'pass ferry corsica corsica linea carte'</li><li>'recharge navigo semaine ratp carte'</li></ul> |
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+ | 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> |
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+ | Leisure & Entertainment / other | <ul><li>'abonnement mensuel canal carte'</li><li>'facture carte du spotify premium carte usa'</li></ul> |
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+ | Food & Drinks / eating out | <ul><li>'facture carte du chez laurette lyon carte'</li><li>'facture carte du le gourmet vegan carte'</li></ul> |
101
+ | Housing / services & maintenance | <ul><li>'prlv sepa renovaction'</li><li>'facture carte du nettoyage professionnel sarl carte'</li></ul> |
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+ | Leisure & Entertainment / travel | <ul><li>'facture carte du air france carte'</li><li>'virement sortant vacation savings for maldives frzzz date'</li></ul> |
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+ | 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> |
104
+ | Investment / other | <ul><li>'achat actions ia revolution carte'</li><li>'participation crowdfunding waterclean projet'</li></ul> |
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+ | Transportation / car loan & leasing | <ul><li>'prelevement sepa creditauto favorisxcb carte'</li><li>'paiement mensualite volkswagen polo v loc vwpolo'</li></ul> |
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+ | Recurrent Payments / subscription | <ul><li>'abonnement vpnsecure net carte'</li><li>'facture carte du adobe creative cloud photo carte'</li></ul> |
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+ | Food & Drinks / other | <ul><li>'payment gourmet popcorn shop carte'</li><li>'achat du confiserie pierre carte'</li></ul> |
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+
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+ ## Evaluation
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+
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+ ### Metrics
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+ | Label | Accuracy |
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+ |:--------|:---------|
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+ | **all** | 0.2727 |
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+
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+ ## Uses
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+
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+ ### Direct Use for Inference
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+
120
+ First install the SetFit library:
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+
122
+ ```bash
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+ pip install setfit
124
+ ```
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+
126
+ Then you can load this model and run inference.
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+
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+ ```python
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+ from setfit import SetFitModel
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+
131
+ # Download from the 🤗 Hub
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+ model = SetFitModel.from_pretrained("HEN10/setfit-particular-transaction-solon-embeddings-labels-large-kaggle-automatisation-v1")
133
+ # Run inference
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+ preds = model("achat académie dressage canin carte")
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+ ```
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+
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+ <!--
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+ ### Downstream Use
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+
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+ *List how someone could finetune this model on their own dataset.*
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+ -->
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+
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+ <!--
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+ ### Out-of-Scope Use
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+
146
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
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+ -->
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+
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+ <!--
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+ ## Bias, Risks and Limitations
151
+
152
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
153
+ -->
154
+
155
+ <!--
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+ ### Recommendations
157
+
158
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
159
+ -->
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+
161
+ ## Training Details
162
+
163
+ ### Training Set Metrics
164
+ | Training set | Min | Median | Max |
165
+ |:-------------|:----|:-------|:----|
166
+ | Word count | 3 | 6.2045 | 10 |
167
+
168
+ | Label | Training Sample Count |
169
+ |:-------------------------------------------|:----------------------|
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+ | Housing / rent | 2 |
171
+ | Housing / house loan | 2 |
172
+ | Housing / utilities & bills | 2 |
173
+ | Housing / services & maintenance | 2 |
174
+ | Housing / other | 2 |
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+ | Food & Drinks / groceries | 2 |
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+ | Food & Drinks / eating out | 2 |
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+ | Food & Drinks / other | 2 |
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+ | Leisure & Entertainment / sports & hobbies | 2 |
179
+ | Leisure & Entertainment / culture & events | 2 |
180
+ | Leisure & Entertainment / travel | 2 |
181
+ | Leisure & Entertainment / other | 2 |
182
+ | Transportation / car loan & leasing | 2 |
183
+ | Transportation / fuel | 2 |
184
+ | Transportation / public transportation | 2 |
185
+ | Transportation / taxi & carpool | 2 |
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+ | Transportation / maitenance | 2 |
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+ | Transportation / other | 2 |
188
+ | Recurrent Payments / loans | 2 |
189
+ | Recurrent Payments / insurance | 2 |
190
+ | Recurrent Payments / subscription | 2 |
191
+ | Recurrent Payments / other | 2 |
192
+ | Investment / securities | 2 |
193
+ | Investment / retirement & savings | 2 |
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+ | Investment / real estate | 2 |
195
+ | Investment / other | 2 |
196
+ | Shopping / clothing | 2 |
197
+ | Shopping / electronics & multimedia | 2 |
198
+ | Shopping / sporting goods | 2 |
199
+ | Shopping / housing equipment | 2 |
200
+ | Shopping / other | 2 |
201
+ | Healthy & Beauty / doctor fees | 2 |
202
+ | Healthy & Beauty / pharmacy | 2 |
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+ | Healthy & Beauty / beauty & self-care | 2 |
204
+ | Healthy & Beauty / veterinary | 2 |
205
+ | Healthy & Beauty / other | 2 |
206
+ | Bank services / transfers | 2 |
207
+ | Bank services / withdrawal | 2 |
208
+ | Bank services / general fees | 2 |
209
+ | Bank services / other | 2 |
210
+ | Other / taxes | 2 |
211
+ | Other / kids | 2 |
212
+ | Other / pets | 2 |
213
+ | Other / other | 2 |
214
+
215
+ ### Training Hyperparameters
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+ - batch_size: (16, 16)
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+ - num_epochs: (1, 1)
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+ - max_steps: -1
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+ - sampling_strategy: oversampling
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+ - body_learning_rate: (2e-05, 1e-05)
221
+ - head_learning_rate: 0.01
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+ - loss: CosineSimilarityLoss
223
+ - distance_metric: cosine_distance
224
+ - margin: 0.25
225
+ - end_to_end: True
226
+ - use_amp: False
227
+ - warmup_proportion: 0.1
228
+ - seed: 6
229
+ - eval_max_steps: -1
230
+ - load_best_model_at_end: False
231
+
232
+ ### Training Results
233
+ | Epoch | Step | Training Loss | Validation Loss |
234
+ |:------:|:----:|:-------------:|:---------------:|
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+ | 0.0021 | 1 | 0.1771 | - |
236
+ | 0.1057 | 50 | 0.1325 | - |
237
+ | 0.2114 | 100 | 0.1132 | - |
238
+ | 0.3171 | 150 | 0.0424 | - |
239
+ | 0.4228 | 200 | 0.0329 | - |
240
+ | 0.5285 | 250 | 0.0581 | - |
241
+ | 0.6342 | 300 | 0.0155 | - |
242
+ | 0.7400 | 350 | 0.0157 | - |
243
+ | 0.8457 | 400 | 0.0138 | - |
244
+ | 0.9514 | 450 | 0.0237 | - |
245
+
246
+ ### Framework Versions
247
+ - Python: 3.10.13
248
+ - SetFit: 1.0.3
249
+ - Sentence Transformers: 2.6.1
250
+ - Transformers: 4.39.3
251
+ - PyTorch: 2.1.2
252
+ - Datasets: 2.17.0
253
+ - Tokenizers: 0.15.2
254
+
255
+ ## Citation
256
+
257
+ ### BibTeX
258
+ ```bibtex
259
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
260
+ doi = {10.48550/ARXIV.2209.11055},
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+ url = {https://arxiv.org/abs/2209.11055},
262
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
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+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
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+ title = {Efficient Few-Shot Learning Without Prompts},
265
+ publisher = {arXiv},
266
+ year = {2022},
267
+ copyright = {Creative Commons Attribution 4.0 International}
268
+ }
269
+ ```
270
+
271
+ <!--
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+ ## Glossary
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+
274
+ *Clearly define terms in order to be accessible across audiences.*
275
+ -->
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+
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+ <!--
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+ ## Model Card Authors
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+
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+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
281
+ -->
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+
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+ <!--
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+ ## Model Card Contact
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+
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+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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+ -->
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+ {
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+ "labels": [
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+ "Housing / rent",
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+ "Housing / house loan",
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+ "Housing / utilities & bills",
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+ "Housing / services & maintenance",
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+ "Housing / other",
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+ "Food & Drinks / groceries",
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+ "Food & Drinks / eating out",
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+ "Food & Drinks / other",
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+ "Leisure & Entertainment / sports & hobbies",
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+ "Leisure & Entertainment / culture & events",
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+ "Leisure & Entertainment / travel",
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+ "Leisure & Entertainment / other",
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+ "Transportation / car loan & leasing",
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+ "Transportation / fuel",
17
+ "Transportation / public transportation",
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+ "Transportation / taxi & carpool",
19
+ "Transportation / maitenance",
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+ "Transportation / other",
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+ "Recurrent Payments / loans",
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+ "Recurrent Payments / insurance",
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+ "Recurrent Payments / subscription",
24
+ "Recurrent Payments / other",
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+ "Investment / securities",
26
+ "Investment / retirement & savings",
27
+ "Investment / real estate",
28
+ "Investment / other",
29
+ "Shopping / clothing",
30
+ "Shopping / electronics & multimedia",
31
+ "Shopping / sporting goods",
32
+ "Shopping / housing equipment",
33
+ "Shopping / other",
34
+ "Healthy & Beauty / doctor fees",
35
+ "Healthy & Beauty / pharmacy",
36
+ "Healthy & Beauty / beauty & self-care",
37
+ "Healthy & Beauty / veterinary",
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+ "Healthy & Beauty / other",
39
+ "Bank services / transfers",
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+ "Bank services / withdrawal",
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+ "Bank services / general fees",
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+ "Bank services / other",
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+ "Other / taxes",
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+ "Other / kids",
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+ "Other / pets",
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+ "Other / other"
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+ ],
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+ "normalize_embeddings": false
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+ }
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+ "do_lower_case": false
4
+ }
special_tokens_map.json ADDED
@@ -0,0 +1,37 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cls_token": {
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+ "content": "[CLS]",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false
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+ },
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+ "mask_token": {
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+ "content": "[MASK]",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false
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+ },
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+ "pad_token": {
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+ "content": "[PAD]",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false
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+ },
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+ "sep_token": {
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+ "content": "[SEP]",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false
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+ },
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+ "unk_token": {
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+ "content": "[UNK]",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false
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+ }
37
+ }
tokenizer.json ADDED
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tokenizer_config.json ADDED
@@ -0,0 +1,64 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
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+ "added_tokens_decoder": {
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+ "0": {
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+ "content": "[PAD]",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ },
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+ "100": {
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+ "content": "[UNK]",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ },
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+ "101": {
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+ "content": "[CLS]",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ },
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+ "102": {
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+ "content": "[SEP]",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ },
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+ "103": {
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+ "content": "[MASK]",
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ }
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+ },
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+ "clean_up_tokenization_spaces": true,
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+ "cls_token": "[CLS]",
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+ "do_basic_tokenize": true,
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+ "do_lower_case": true,
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+ "mask_token": "[MASK]",
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+ "max_length": 128,
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+ "model_max_length": 512,
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+ "never_split": null,
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+ "pad_to_multiple_of": null,
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+ "pad_token": "[PAD]",
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+ "pad_token_type_id": 0,
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+ "padding_side": "right",
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+ "sep_token": "[SEP]",
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+ "stride": 0,
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+ "strip_accents": null,
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+ "tokenize_chinese_chars": true,
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+ "tokenizer_class": "BertTokenizer",
61
+ "truncation_side": "right",
62
+ "truncation_strategy": "longest_first",
63
+ "unk_token": "[UNK]"
64
+ }
vocab.txt ADDED
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