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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: prlv sepa ecole montaigne cotisation scolaire |
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- text: facture carte du pharmacie pont neuf carte |
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- text: virement sortant facture soleil energie |
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- text: leçon de surf hossegor surf club carte |
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- text: virement initie application mobile vers comptes joints |
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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: 0.7007575757575758 |
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name: Accuracy |
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--- |
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# SetFit |
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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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The model has been trained using an efficient few-shot learning technique that involves: |
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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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## Model Details |
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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:** 512 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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### Model Sources |
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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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### Model Labels |
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| Label | Examples | |
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|:-------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| Other / kids | <ul><li>'cantine scolaire mars paiement carte'</li><li>'virement recu alouette garde d enfants'</li><li>'achat carte couture kids atelier carte'</li></ul> | |
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| Bank services / withdrawal | <ul><li>'retrait dab banque du peuple lille carte fr'</li><li>'retrait dab megastore electronix carte'</li><li>'retrait dab banqueville centre carte'</li></ul> | |
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| Housing / rent | <ul><li>'reglement loyer f nice carte'</li><li>'paiement loyer duplex nantes carte'</li><li>'loyer t bordeaux chartrons carte'</li></ul> | |
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| Leisure & Entertainment / sports & hobbies | <ul><li>'abonnement golf de roncevaux avril carte'</li><li>'paiement en ligne du adidas fr carte'</li><li>'cours de tennis indoor club fontenay carte'</li></ul> | |
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| Transportation / car loan & leasing | <ul><li>'prelevement leaseplan citroen c'</li><li>'loyer mensuel volkswagen polo carte'</li><li>'pret vehicule citroen c carte'</li></ul> | |
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| Healthy & Beauty / veterinary | <ul><li>'urgence digestive bibou urgencyvet carte'</li><li>'echoradiographie minette carte'</li><li>'consultation dermatologique canine vetderm carte'</li></ul> | |
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| Transportation / taxi & carpool | <ul><li>'facture carte du grab bangkok carte tha thb commission'</li><li>'prlv sepa bolt transport carte'</li><li>'facture carte du kakao taxi seoul carte kor krw commission'</li></ul> | |
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| Healthy & Beauty / doctor fees | <ul><li>'consultation dr lemoine carte'</li><li>'rdv pediatrie dr lenoir carte'</li><li>'rdv chirurgie esthetique dr martin carte'</li></ul> | |
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| Food & Drinks / eating out | <ul><li>'facture carte du vietnam delices strasbourg carte'</li><li>'facture carte du la cabane creole la reunion carte'</li><li>'facture carte du the green pub montpellier carte'</li></ul> | |
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| Transportation / other | <ul><li>'location velo elecgreen du paris carte'</li><li>'frais de lavage auto eco wash carte'</li><li>'frais post stationnement carte'</li></ul> | |
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| Healthy & Beauty / beauty & self-care | <ul><li>'achat lush cosmetiques lyon carte'</li><li>'facture carte du douglas beauty store carte'</li><li>'achat salon de coiffure elegance carte'</li></ul> | |
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| Bank services / other | <ul><li>'cotisation carte gold annuelle carte'</li><li>'frais emission duplicata rib iban carte'</li><li>'frais mise en place autorisation de decouvert carte'</li></ul> | |
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| Bank services / general fees | <ul><li>'frais de gestion compte epargne banqplus carte'</li><li>'abonnement service banque en ligne banqfacile carte'</li><li>'frais sur decouvert autorise'</li></ul> | |
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| Leisure & Entertainment / culture & events | <ul><li>'pass festival avignon off carte'</li><li>'reservation carte salon du livre paris carte'</li><li>'achat carte billet expo universselle carte'</li></ul> | |
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| Other / taxes | <ul><li>'taxe d amenagement projet xyz'</li><li>'taxe annuelle sur les locations meublees non professionnelles'</li><li>'impot sur le revenu prelevement sepa fiscal frzzz'</li></ul> | |
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| Housing / services & maintenance | <ul><li>'prlv sepa sos plombier paris'</li><li>'virement recu travaux peinture dupont michel'</li><li>'facture carte du vitrerie lumiere carte'</li></ul> | |
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| Housing / utilities & bills | <ul><li>'prlv sepa edf facture electricite'</li><li>'prlv sepa engie'</li><li>'facture carte du bouygues telecom carte'</li></ul> | |
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| Investment / real estate | <ul><li>'reservation studio montagne carte'</li><li>'virement sortant achat terrain bellevue carte'</li><li>'prlv sepa agence immobiliere commission vente'</li></ul> | |
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| Recurrent Payments / subscription | <ul><li>'abonnement annual magazine cuisine carte'</li><li>'abonnement plateforme d apprentissage en ligne skillex carte'</li><li>'adhesion annuelle amazon prime carte'</li></ul> | |
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| Other / other | <ul><li>'paiement service debrisage encombrants'</li><li>'stage de survie nature extreme carte'</li><li>'inscription marathon de paris'</li></ul> | |
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| Shopping / electronics & multimedia | <ul><li>'achat carte du gamerzone carte'</li><li>'facture carte du hightech store paris carte'</li><li>'achat en ligne hp store carte usa'</li></ul> | |
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| Bank services / transfers | <ul><li>'virement sortant facture soleil energie'</li><li>'transfer economies vers pel'</li><li>'virement initie application mobile vers comptes joints'</li></ul> | |
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| Investment / retirement & savings | <ul><li>'versement plan epargne retraite crédit agricole carte'</li><li>'achat parts cooperative eparco'</li><li>'souscription fonds pension'</li></ul> | |
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| Housing / other | <ul><li>'achat mobilier jardin bleutropic carte'</li><li>'douchette ecologique ecoflow carte'</li><li>'virement recu du remboursement depot de garantie'</li></ul> | |
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| Housing / house loan | <ul><li>'solde emprunt habitat fortuneo pret'</li><li>'remboursement emprunt logis credit agricole'</li><li>'transaction pret immo societe generale de prêteur frsge'</li></ul> | |
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| Recurrent Payments / other | <ul><li>'don mensuel a l ong environ actionfr transaction date'</li><li>'contribution trimestrielle revue culturelle lumières date'</li><li>'prlv sepa chess com premium'</li></ul> | |
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| Transportation / fuel | <ul><li>'facture carte du bp energy carte'</li><li>'depense s c agip lyon carte'</li><li>'debit carte circle k energy carte'</li></ul> | |
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| Other / pets | <ul><li>'debit automatique assurance chien fido protect'</li><li>'retrait dab ferme des lapinous carte commission'</li><li>'achat animalerie toutouplus lyon carte'</li></ul> | |
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| Transportation / maitenance | <ul><li>'reparation systeme de navigation gps auto tech dijon'</li><li>'vidange huile moteur garage du centre rennes'</li><li>'paiement carte du garage rénov clim reims carte'</li></ul> | |
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| Food & Drinks / groceries | <ul><li>'facture carte du fromagerie dupont carte'</li><li>'facture carte du cremerie des alpes carte'</li><li>'prlv sepa la ferme lorraine'</li></ul> | |
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| Recurrent Payments / insurance | <ul><li>'cotisation assurance multirisque domus secur frzzz'</li><li>'prelevement sepa assurance grand voyage worldtravel frzzz'</li><li>'prlv sepa assurance emprunteur bnp paribas'</li></ul> | |
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| Food & Drinks / other | <ul><li>'facture carte just press carte'</li><li>'achat card du tea box paris carte'</li><li>'facture carte du café de flore carte'</li></ul> | |
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| Recurrent Payments / loans | <ul><li>'prlv sepa monabanq pret amelioration habitat frzzz'</li><li>'prélèvement sepa credit agricole pret immoparc carte'</li><li>'prlv sepa sofinco pret conso carte'</li></ul> | |
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| Transportation / public transportation | <ul><li>'achat titres v ville de lille carte'</li><li>'navette aeroport orly bus carte'</li><li>'ticket voyages lyon transport carte'</li></ul> | |
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| Investment / securities | <ul><li>'souscription fonds pension carte'</li><li>'vente sicav monetaire carte'</li><li>'achat parts initiative europe carte'</li></ul> | |
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| Shopping / housing equipment | <ul><li>'paiement cb darty nice carte'</li><li>'achat au moulin des peintures nantes carte'</li><li>'achat cb castorama lille carte le'</li></ul> | |
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| Healthy & Beauty / other | <ul><li>'abonnement annuel gymnase formeplus carte'</li><li>'seance yoga luxe paris carte'</li><li>'soin relaxant doux jardin nantes carte'</li></ul> | |
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| Healthy & Beauty / pharmacy | <ul><li>'prlv sepa pharmacie azureech'</li><li>'facture carte du pharmacie centrale paris carte'</li><li>'achat pharmacie du parc carte le'</li></ul> | |
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| Shopping / clothing | <ul><li>'facture carte du levis store carte can cad commission'</li><li>'achat veste en cuir chez massimo dutti carte paris'</li><li>'facture carte converse paris carte'</li></ul> | |
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| Shopping / sporting goods | <ul><li>'achat decathlon besancon carte'</li><li>'achat carte nike store carte'</li><li>'paiement carte running store paris carte'</li></ul> | |
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| Leisure & Entertainment / travel | <ul><li>'facture carte du seaworld san diego carte'</li><li>'payment card louvre museum card'</li><li>'retrait dab cairo airport card egy egp commission'</li></ul> | |
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| Investment / other | <ul><li>'achat parts sociales cooperative agricole carte'</li><li>'achat actions ia revolution carte'</li><li>'investissement projet ecologique carte'</li></ul> | |
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| Leisure & Entertainment / other | <ul><li>'facture carte du hbo max carte usa'</li><li>'abonnement carte playstation plus carte eu'</li><li>'abonnement mensuel canal carte'</li></ul> | |
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| Shopping / other | <ul><li>'facture carte du fnac livres carte'</li><li>'facture carte du les tresors de sophie bordeaux carte'</li><li>'achat jouets et merveilles dijon carte'</li></ul> | |
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## Evaluation |
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### Metrics |
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| Label | Accuracy | |
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|:--------|:---------| |
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| **all** | 0.7008 | |
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## Uses |
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### Direct Use for Inference |
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First install the SetFit library: |
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```bash |
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pip install setfit |
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``` |
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Then you can load this model and run inference. |
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```python |
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from setfit import SetFitModel |
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# Download from the 🤗 Hub |
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model = SetFitModel.from_pretrained("HEN10/setfit-particular-transaction-solon-embeddings-labels-large-v4") |
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# Run inference |
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preds = model("leçon de surf hossegor surf club carte") |
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``` |
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<!-- |
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### Downstream Use |
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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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### Out-of-Scope Use |
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*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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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Training Set Metrics |
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| Training set | Min | Median | Max | |
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|:-------------|:----|:-------|:----| |
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| Word count | 3 | 5.9159 | 12 | |
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| Label | Training Sample Count | |
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|:-------------------------------------------|:----------------------| |
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| Housing / rent | 20 | |
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| Housing / house loan | 20 | |
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| Housing / utilities & bills | 20 | |
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| Housing / services & maintenance | 20 | |
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| Housing / other | 20 | |
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| Food & Drinks / groceries | 20 | |
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| Food & Drinks / eating out | 20 | |
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| Food & Drinks / other | 20 | |
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| Leisure & Entertainment / sports & hobbies | 20 | |
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| Leisure & Entertainment / culture & events | 20 | |
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| Leisure & Entertainment / travel | 20 | |
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| Leisure & Entertainment / other | 20 | |
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| Transportation / car loan & leasing | 20 | |
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| Transportation / fuel | 20 | |
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| Transportation / public transportation | 20 | |
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| Transportation / taxi & carpool | 20 | |
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| Transportation / maitenance | 20 | |
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| Transportation / other | 20 | |
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| Recurrent Payments / loans | 20 | |
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| Recurrent Payments / insurance | 20 | |
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| Recurrent Payments / subscription | 20 | |
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| Recurrent Payments / other | 20 | |
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| Investment / securities | 20 | |
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| Investment / retirement & savings | 20 | |
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| Investment / real estate | 20 | |
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| Investment / other | 20 | |
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| Shopping / clothing | 20 | |
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| Shopping / electronics & multimedia | 20 | |
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| Shopping / sporting goods | 20 | |
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| Shopping / housing equipment | 20 | |
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| Shopping / other | 20 | |
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| Healthy & Beauty / doctor fees | 20 | |
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| Healthy & Beauty / pharmacy | 20 | |
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| Healthy & Beauty / beauty & self-care | 20 | |
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| Healthy & Beauty / veterinary | 20 | |
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| Healthy & Beauty / other | 20 | |
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| Bank services / transfers | 20 | |
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| Bank services / withdrawal | 20 | |
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| Bank services / general fees | 20 | |
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| Bank services / other | 20 | |
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| Other / taxes | 20 | |
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| Other / kids | 20 | |
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| Other / pets | 20 | |
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| Other / other | 20 | |
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### Training Hyperparameters |
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- batch_size: (26, 26) |
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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) |
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- head_learning_rate: 0.01 |
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- loss: CosineSimilarityLoss |
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- distance_metric: cosine_distance |
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- margin: 0.25 |
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- end_to_end: True |
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- use_amp: False |
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- warmup_proportion: 0.1 |
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- seed: 6 |
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- eval_max_steps: -1 |
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- load_best_model_at_end: False |
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### Training Results |
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| Epoch | Step | Training Loss | Validation Loss | |
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|:------:|:-----:|:-------------:|:---------------:| |
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| 0.0000 | 1 | 0.2084 | - | |
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| 0.0012 | 50 | 0.2041 | - | |
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| 0.0000 | 1 | 0.1841 | - | |
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| 0.0017 | 50 | 0.219 | - | |
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| 0.0034 | 100 | 0.2197 | - | |
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| 0.0052 | 150 | 0.1724 | - | |
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| 0.0069 | 200 | 0.2291 | - | |
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| 0.0086 | 250 | 0.1693 | - | |
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| 0.0103 | 300 | 0.0832 | - | |
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| 0.0120 | 350 | 0.1414 | - | |
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| 0.0137 | 400 | 0.0989 | - | |
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| 0.0155 | 450 | 0.0962 | - | |
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| 0.0172 | 500 | 0.1132 | - | |
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| 0.0189 | 550 | 0.1 | - | |
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| 0.0206 | 600 | 0.0561 | - | |
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| 0.0223 | 650 | 0.0851 | - | |
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| 0.0240 | 700 | 0.0762 | - | |
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| 0.0258 | 750 | 0.0876 | - | |
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| 0.0275 | 800 | 0.0414 | - | |
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| 0.0292 | 850 | 0.0368 | - | |
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| 0.0309 | 900 | 0.0409 | - | |
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| 0.0326 | 950 | 0.0212 | - | |
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| 0.0344 | 1000 | 0.0175 | - | |
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| 0.0361 | 1050 | 0.05 | - | |
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| 0.0378 | 1100 | 0.0848 | - | |
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| 0.0395 | 1150 | 0.0549 | - | |
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| 0.0412 | 1200 | 0.0395 | - | |
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| 0.0429 | 1250 | 0.029 | - | |
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| 0.0447 | 1300 | 0.0047 | - | |
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| 0.0464 | 1350 | 0.0387 | - | |
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| 0.0481 | 1400 | 0.0268 | - | |
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| 0.0498 | 1450 | 0.0531 | - | |
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| 0.0515 | 1500 | 0.0038 | - | |
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| 0.0532 | 1550 | 0.0226 | - | |
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| 0.0550 | 1600 | 0.0349 | - | |
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| 0.0567 | 1650 | 0.0106 | - | |
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| 0.0584 | 1700 | 0.0049 | - | |
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| 0.0601 | 1750 | 0.0171 | - | |
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| 0.0618 | 1800 | 0.0066 | - | |
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| 0.0636 | 1850 | 0.0066 | - | |
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| 0.0653 | 1900 | 0.0039 | - | |
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| 0.0670 | 1950 | 0.0016 | - | |
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| 0.0687 | 2000 | 0.0414 | - | |
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| 0.0704 | 2050 | 0.0172 | - | |
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| 0.0721 | 2100 | 0.0039 | - | |
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| 0.0739 | 2150 | 0.0036 | - | |
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| 0.0756 | 2200 | 0.0334 | - | |
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| 0.0773 | 2250 | 0.0025 | - | |
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| 0.0790 | 2300 | 0.0022 | - | |
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| 0.0807 | 2350 | 0.0017 | - | |
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| 0.0825 | 2400 | 0.0015 | - | |
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| 0.0842 | 2450 | 0.0125 | - | |
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| 0.0859 | 2500 | 0.0023 | - | |
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| 0.0876 | 2550 | 0.0023 | - | |
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| 0.0893 | 2600 | 0.0013 | - | |
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| 0.0910 | 2650 | 0.0728 | - | |
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| 0.0928 | 2700 | 0.0141 | - | |
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| 0.0945 | 2750 | 0.0332 | - | |
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| 0.0962 | 2800 | 0.0632 | - | |
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| 0.0979 | 2850 | 0.0042 | - | |
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| 0.0996 | 2900 | 0.0117 | - | |
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| 0.1013 | 2950 | 0.0014 | - | |
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| 0.1031 | 3000 | 0.0013 | - | |
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| 0.1048 | 3050 | 0.0464 | - | |
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| 0.1065 | 3100 | 0.0031 | - | |
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| 0.1082 | 3150 | 0.0007 | - | |
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| 0.1099 | 3200 | 0.0008 | - | |
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| 0.1117 | 3250 | 0.001 | - | |
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| 0.1134 | 3300 | 0.001 | - | |
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| 0.1151 | 3350 | 0.0016 | - | |
|
| 0.1168 | 3400 | 0.0006 | - | |
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| 0.1185 | 3450 | 0.0005 | - | |
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| 0.1202 | 3500 | 0.0006 | - | |
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| 0.1220 | 3550 | 0.0008 | - | |
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| 0.1237 | 3600 | 0.0368 | - | |
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| 0.1254 | 3650 | 0.0026 | - | |
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| 0.1271 | 3700 | 0.0372 | - | |
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| 0.1288 | 3750 | 0.0006 | - | |
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| 0.1305 | 3800 | 0.0005 | - | |
|
| 0.1323 | 3850 | 0.0276 | - | |
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| 0.1340 | 3900 | 0.0007 | - | |
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| 0.1357 | 3950 | 0.0013 | - | |
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| 0.1374 | 4000 | 0.0008 | - | |
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| 0.1391 | 4050 | 0.0018 | - | |
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| 0.1409 | 4100 | 0.0292 | - | |
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| 0.1426 | 4150 | 0.0102 | - | |
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| 0.1443 | 4200 | 0.0093 | - | |
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| 0.1460 | 4250 | 0.0022 | - | |
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| 0.1477 | 4300 | 0.0032 | - | |
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| 0.1494 | 4350 | 0.001 | - | |
|
| 0.1512 | 4400 | 0.0006 | - | |
|
| 0.1529 | 4450 | 0.0007 | - | |
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| 0.1546 | 4500 | 0.0007 | - | |
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| 0.1563 | 4550 | 0.0007 | - | |
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| 0.1580 | 4600 | 0.0007 | - | |
|
| 0.1597 | 4650 | 0.0011 | - | |
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| 0.1615 | 4700 | 0.0008 | - | |
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| 0.1632 | 4750 | 0.0374 | - | |
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| 0.1649 | 4800 | 0.0004 | - | |
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| 0.1666 | 4850 | 0.0008 | - | |
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| 0.1683 | 4900 | 0.005 | - | |
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| 0.1701 | 4950 | 0.0013 | - | |
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| 0.1718 | 5000 | 0.0016 | - | |
|
| 0.1735 | 5050 | 0.0006 | - | |
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| 0.1752 | 5100 | 0.0007 | - | |
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| 0.1769 | 5150 | 0.0007 | - | |
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| 0.1786 | 5200 | 0.0004 | - | |
|
| 0.1804 | 5250 | 0.0003 | - | |
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| 0.1821 | 5300 | 0.0004 | - | |
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| 0.1838 | 5350 | 0.0004 | - | |
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| 0.1855 | 5400 | 0.0002 | - | |
|
| 0.1872 | 5450 | 0.036 | - | |
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| 0.1890 | 5500 | 0.0003 | - | |
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| 0.1907 | 5550 | 0.0003 | - | |
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| 0.1924 | 5600 | 0.0003 | - | |
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| 0.1941 | 5650 | 0.0006 | - | |
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| 0.1958 | 5700 | 0.0005 | - | |
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| 0.1975 | 5750 | 0.0057 | - | |
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| 0.1993 | 5800 | 0.0008 | - | |
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| 0.2010 | 5850 | 0.0002 | - | |
|
| 0.2027 | 5900 | 0.0013 | - | |
|
| 0.2044 | 5950 | 0.0004 | - | |
|
| 0.2061 | 6000 | 0.0002 | - | |
|
| 0.2078 | 6050 | 0.0002 | - | |
|
| 0.2096 | 6100 | 0.0015 | - | |
|
| 0.2113 | 6150 | 0.037 | - | |
|
| 0.2130 | 6200 | 0.0003 | - | |
|
| 0.2147 | 6250 | 0.0003 | - | |
|
| 0.2164 | 6300 | 0.0002 | - | |
|
| 0.2182 | 6350 | 0.0003 | - | |
|
| 0.2199 | 6400 | 0.0005 | - | |
|
| 0.2216 | 6450 | 0.0004 | - | |
|
| 0.2233 | 6500 | 0.0042 | - | |
|
| 0.2250 | 6550 | 0.0004 | - | |
|
| 0.2267 | 6600 | 0.0006 | - | |
|
| 0.2285 | 6650 | 0.0004 | - | |
|
| 0.2302 | 6700 | 0.0005 | - | |
|
| 0.2319 | 6750 | 0.0021 | - | |
|
| 0.2336 | 6800 | 0.0003 | - | |
|
| 0.2353 | 6850 | 0.0003 | - | |
|
| 0.2370 | 6900 | 0.0005 | - | |
|
| 0.2388 | 6950 | 0.0003 | - | |
|
| 0.2405 | 7000 | 0.0002 | - | |
|
| 0.2422 | 7050 | 0.0003 | - | |
|
| 0.2439 | 7100 | 0.0004 | - | |
|
| 0.2456 | 7150 | 0.0005 | - | |
|
| 0.2474 | 7200 | 0.0005 | - | |
|
| 0.2491 | 7250 | 0.001 | - | |
|
| 0.2508 | 7300 | 0.0055 | - | |
|
| 0.2525 | 7350 | 0.0005 | - | |
|
| 0.2542 | 7400 | 0.0005 | - | |
|
| 0.2559 | 7450 | 0.0007 | - | |
|
| 0.2577 | 7500 | 0.0002 | - | |
|
| 0.2594 | 7550 | 0.0745 | - | |
|
| 0.2611 | 7600 | 0.0003 | - | |
|
| 0.2628 | 7650 | 0.0002 | - | |
|
| 0.2645 | 7700 | 0.0002 | - | |
|
| 0.2662 | 7750 | 0.0004 | - | |
|
| 0.2680 | 7800 | 0.0002 | - | |
|
| 0.2697 | 7850 | 0.0002 | - | |
|
| 0.2714 | 7900 | 0.0003 | - | |
|
| 0.2731 | 7950 | 0.0002 | - | |
|
| 0.2748 | 8000 | 0.0002 | - | |
|
| 0.2766 | 8050 | 0.0003 | - | |
|
| 0.2783 | 8100 | 0.0003 | - | |
|
| 0.2800 | 8150 | 0.0313 | - | |
|
| 0.2817 | 8200 | 0.0007 | - | |
|
| 0.2834 | 8250 | 0.0002 | - | |
|
| 0.2851 | 8300 | 0.0003 | - | |
|
| 0.2869 | 8350 | 0.0003 | - | |
|
| 0.2886 | 8400 | 0.0003 | - | |
|
| 0.2903 | 8450 | 0.0002 | - | |
|
| 0.2920 | 8500 | 0.0003 | - | |
|
| 0.2937 | 8550 | 0.0154 | - | |
|
| 0.2955 | 8600 | 0.0003 | - | |
|
| 0.2972 | 8650 | 0.0005 | - | |
|
| 0.2989 | 8700 | 0.0041 | - | |
|
| 0.3006 | 8750 | 0.0003 | - | |
|
| 0.3023 | 8800 | 0.0002 | - | |
|
| 0.3040 | 8850 | 0.0003 | - | |
|
| 0.3058 | 8900 | 0.0001 | - | |
|
| 0.3075 | 8950 | 0.0005 | - | |
|
| 0.3092 | 9000 | 0.0022 | - | |
|
| 0.3109 | 9050 | 0.0002 | - | |
|
| 0.3126 | 9100 | 0.0003 | - | |
|
| 0.3143 | 9150 | 0.0002 | - | |
|
| 0.3161 | 9200 | 0.0001 | - | |
|
| 0.3178 | 9250 | 0.0002 | - | |
|
| 0.3195 | 9300 | 0.0001 | - | |
|
| 0.3212 | 9350 | 0.0002 | - | |
|
| 0.3229 | 9400 | 0.0002 | - | |
|
| 0.3247 | 9450 | 0.0003 | - | |
|
| 0.3264 | 9500 | 0.0017 | - | |
|
| 0.3281 | 9550 | 0.003 | - | |
|
| 0.3298 | 9600 | 0.0039 | - | |
|
| 0.3315 | 9650 | 0.0028 | - | |
|
| 0.3332 | 9700 | 0.0037 | - | |
|
| 0.3350 | 9750 | 0.0005 | - | |
|
| 0.3367 | 9800 | 0.0352 | - | |
|
| 0.3384 | 9850 | 0.0006 | - | |
|
| 0.3401 | 9900 | 0.0006 | - | |
|
| 0.3418 | 9950 | 0.0004 | - | |
|
| 0.3435 | 10000 | 0.0002 | - | |
|
| 0.3453 | 10050 | 0.0012 | - | |
|
| 0.3470 | 10100 | 0.0002 | - | |
|
| 0.3487 | 10150 | 0.0003 | - | |
|
| 0.3504 | 10200 | 0.0002 | - | |
|
| 0.3521 | 10250 | 0.0002 | - | |
|
| 0.3539 | 10300 | 0.0004 | - | |
|
| 0.3556 | 10350 | 0.0003 | - | |
|
| 0.3573 | 10400 | 0.0003 | - | |
|
| 0.3590 | 10450 | 0.0002 | - | |
|
| 0.3607 | 10500 | 0.0004 | - | |
|
| 0.3624 | 10550 | 0.0004 | - | |
|
| 0.3642 | 10600 | 0.0371 | - | |
|
| 0.3659 | 10650 | 0.0005 | - | |
|
| 0.3676 | 10700 | 0.0236 | - | |
|
| 0.3693 | 10750 | 0.0002 | - | |
|
| 0.3710 | 10800 | 0.0002 | - | |
|
| 0.3727 | 10850 | 0.0003 | - | |
|
| 0.3745 | 10900 | 0.0004 | - | |
|
| 0.3762 | 10950 | 0.0002 | - | |
|
| 0.3779 | 11000 | 0.0002 | - | |
|
| 0.3796 | 11050 | 0.0002 | - | |
|
| 0.3813 | 11100 | 0.0001 | - | |
|
| 0.3831 | 11150 | 0.0001 | - | |
|
| 0.3848 | 11200 | 0.0002 | - | |
|
| 0.3865 | 11250 | 0.0002 | - | |
|
| 0.3882 | 11300 | 0.0001 | - | |
|
| 0.3899 | 11350 | 0.0001 | - | |
|
| 0.3916 | 11400 | 0.0351 | - | |
|
| 0.3934 | 11450 | 0.0003 | - | |
|
| 0.3951 | 11500 | 0.0001 | - | |
|
| 0.3968 | 11550 | 0.0326 | - | |
|
| 0.3985 | 11600 | 0.0001 | - | |
|
| 0.4002 | 11650 | 0.0006 | - | |
|
| 0.4020 | 11700 | 0.0002 | - | |
|
| 0.4037 | 11750 | 0.0004 | - | |
|
| 0.4054 | 11800 | 0.0002 | - | |
|
| 0.4071 | 11850 | 0.0002 | - | |
|
| 0.4088 | 11900 | 0.0001 | - | |
|
| 0.4105 | 11950 | 0.0002 | - | |
|
| 0.4123 | 12000 | 0.0002 | - | |
|
| 0.4140 | 12050 | 0.0003 | - | |
|
| 0.4157 | 12100 | 0.0003 | - | |
|
| 0.4174 | 12150 | 0.0001 | - | |
|
| 0.4191 | 12200 | 0.0001 | - | |
|
| 0.4208 | 12250 | 0.0003 | - | |
|
| 0.4226 | 12300 | 0.0001 | - | |
|
| 0.4243 | 12350 | 0.0002 | - | |
|
| 0.4260 | 12400 | 0.0003 | - | |
|
| 0.4277 | 12450 | 0.0002 | - | |
|
| 0.4294 | 12500 | 0.0002 | - | |
|
| 0.4312 | 12550 | 0.0002 | - | |
|
| 0.4329 | 12600 | 0.0002 | - | |
|
| 0.4346 | 12650 | 0.0007 | - | |
|
| 0.4363 | 12700 | 0.0002 | - | |
|
| 0.4380 | 12750 | 0.0003 | - | |
|
| 0.4397 | 12800 | 0.0001 | - | |
|
| 0.4415 | 12850 | 0.0001 | - | |
|
| 0.4432 | 12900 | 0.0002 | - | |
|
| 0.4449 | 12950 | 0.001 | - | |
|
| 0.4466 | 13000 | 0.0002 | - | |
|
| 0.4483 | 13050 | 0.0002 | - | |
|
| 0.4500 | 13100 | 0.0005 | - | |
|
| 0.4518 | 13150 | 0.0002 | - | |
|
| 0.4535 | 13200 | 0.0002 | - | |
|
| 0.4552 | 13250 | 0.0001 | - | |
|
| 0.4569 | 13300 | 0.0003 | - | |
|
| 0.4586 | 13350 | 0.0013 | - | |
|
| 0.4604 | 13400 | 0.0002 | - | |
|
| 0.4621 | 13450 | 0.0372 | - | |
|
| 0.4638 | 13500 | 0.0002 | - | |
|
| 0.4655 | 13550 | 0.0003 | - | |
|
| 0.4672 | 13600 | 0.0025 | - | |
|
| 0.4689 | 13650 | 0.0002 | - | |
|
| 0.4707 | 13700 | 0.0002 | - | |
|
| 0.4724 | 13750 | 0.0001 | - | |
|
| 0.4741 | 13800 | 0.0002 | - | |
|
| 0.4758 | 13850 | 0.0001 | - | |
|
| 0.4775 | 13900 | 0.0003 | - | |
|
| 0.4792 | 13950 | 0.0026 | - | |
|
| 0.4810 | 14000 | 0.0002 | - | |
|
| 0.4827 | 14050 | 0.0002 | - | |
|
| 0.4844 | 14100 | 0.0002 | - | |
|
| 0.4861 | 14150 | 0.0002 | - | |
|
| 0.4878 | 14200 | 0.0002 | - | |
|
| 0.4896 | 14250 | 0.0002 | - | |
|
| 0.4913 | 14300 | 0.0003 | - | |
|
| 0.4930 | 14350 | 0.0002 | - | |
|
| 0.4947 | 14400 | 0.0014 | - | |
|
| 0.4964 | 14450 | 0.0002 | - | |
|
| 0.4981 | 14500 | 0.0001 | - | |
|
| 0.4999 | 14550 | 0.0002 | - | |
|
| 0.5016 | 14600 | 0.0001 | - | |
|
| 0.5033 | 14650 | 0.0002 | - | |
|
| 0.5050 | 14700 | 0.0001 | - | |
|
| 0.5067 | 14750 | 0.0002 | - | |
|
| 0.5085 | 14800 | 0.0001 | - | |
|
| 0.5102 | 14850 | 0.0001 | - | |
|
| 0.5119 | 14900 | 0.0002 | - | |
|
| 0.5136 | 14950 | 0.0001 | - | |
|
| 0.5153 | 15000 | 0.0001 | - | |
|
| 0.5170 | 15050 | 0.0001 | - | |
|
| 0.5188 | 15100 | 0.0002 | - | |
|
| 0.5205 | 15150 | 0.0002 | - | |
|
| 0.5222 | 15200 | 0.0002 | - | |
|
| 0.5239 | 15250 | 0.0001 | - | |
|
| 0.5256 | 15300 | 0.0001 | - | |
|
| 0.5273 | 15350 | 0.0001 | - | |
|
| 0.5291 | 15400 | 0.0001 | - | |
|
| 0.5308 | 15450 | 0.0001 | - | |
|
| 0.5325 | 15500 | 0.0001 | - | |
|
| 0.5342 | 15550 | 0.0001 | - | |
|
| 0.5359 | 15600 | 0.0001 | - | |
|
| 0.5377 | 15650 | 0.0001 | - | |
|
| 0.5394 | 15700 | 0.0001 | - | |
|
| 0.5411 | 15750 | 0.0001 | - | |
|
| 0.5428 | 15800 | 0.0001 | - | |
|
| 0.5445 | 15850 | 0.0002 | - | |
|
| 0.5462 | 15900 | 0.0002 | - | |
|
| 0.5480 | 15950 | 0.0001 | - | |
|
| 0.5497 | 16000 | 0.0001 | - | |
|
| 0.5514 | 16050 | 0.0001 | - | |
|
| 0.5531 | 16100 | 0.0001 | - | |
|
| 0.5548 | 16150 | 0.0001 | - | |
|
| 0.5565 | 16200 | 0.0001 | - | |
|
| 0.5583 | 16250 | 0.0001 | - | |
|
| 0.5600 | 16300 | 0.0001 | - | |
|
| 0.5617 | 16350 | 0.0001 | - | |
|
| 0.5634 | 16400 | 0.0002 | - | |
|
| 0.5651 | 16450 | 0.0001 | - | |
|
| 0.5669 | 16500 | 0.0001 | - | |
|
| 0.5686 | 16550 | 0.0001 | - | |
|
| 0.5703 | 16600 | 0.0001 | - | |
|
| 0.5720 | 16650 | 0.0002 | - | |
|
| 0.5737 | 16700 | 0.0001 | - | |
|
| 0.5754 | 16750 | 0.0001 | - | |
|
| 0.5772 | 16800 | 0.0001 | - | |
|
| 0.5789 | 16850 | 0.0001 | - | |
|
| 0.5806 | 16900 | 0.0001 | - | |
|
| 0.5823 | 16950 | 0.0001 | - | |
|
| 0.5840 | 17000 | 0.0001 | - | |
|
| 0.5857 | 17050 | 0.0002 | - | |
|
| 0.5875 | 17100 | 0.0001 | - | |
|
| 0.5892 | 17150 | 0.0001 | - | |
|
| 0.5909 | 17200 | 0.0001 | - | |
|
| 0.5926 | 17250 | 0.0001 | - | |
|
| 0.5943 | 17300 | 0.0001 | - | |
|
| 0.5961 | 17350 | 0.0001 | - | |
|
| 0.5978 | 17400 | 0.0001 | - | |
|
| 0.5995 | 17450 | 0.0001 | - | |
|
| 0.6012 | 17500 | 0.0371 | - | |
|
| 0.6029 | 17550 | 0.0001 | - | |
|
| 0.6046 | 17600 | 0.0001 | - | |
|
| 0.6064 | 17650 | 0.0001 | - | |
|
| 0.6081 | 17700 | 0.0001 | - | |
|
| 0.6098 | 17750 | 0.0001 | - | |
|
| 0.6115 | 17800 | 0.0002 | - | |
|
| 0.6132 | 17850 | 0.0007 | - | |
|
| 0.6150 | 17900 | 0.0002 | - | |
|
| 0.6167 | 17950 | 0.0001 | - | |
|
| 0.6184 | 18000 | 0.0115 | - | |
|
| 0.6201 | 18050 | 0.0001 | - | |
|
| 0.6218 | 18100 | 0.0004 | - | |
|
| 0.6235 | 18150 | 0.0002 | - | |
|
| 0.6253 | 18200 | 0.0074 | - | |
|
| 0.6270 | 18250 | 0.0325 | - | |
|
| 0.6287 | 18300 | 0.0008 | - | |
|
| 0.6304 | 18350 | 0.0007 | - | |
|
| 0.6321 | 18400 | 0.0002 | - | |
|
| 0.6338 | 18450 | 0.0005 | - | |
|
| 0.6356 | 18500 | 0.0003 | - | |
|
| 0.6373 | 18550 | 0.0003 | - | |
|
| 0.6390 | 18600 | 0.0002 | - | |
|
| 0.6407 | 18650 | 0.0003 | - | |
|
| 0.6424 | 18700 | 0.0003 | - | |
|
| 0.6442 | 18750 | 0.0002 | - | |
|
| 0.6459 | 18800 | 0.0002 | - | |
|
| 0.6476 | 18850 | 0.0002 | - | |
|
| 0.6493 | 18900 | 0.0002 | - | |
|
| 0.6510 | 18950 | 0.0001 | - | |
|
| 0.6527 | 19000 | 0.0001 | - | |
|
| 0.6545 | 19050 | 0.0003 | - | |
|
| 0.6562 | 19100 | 0.0001 | - | |
|
| 0.6579 | 19150 | 0.0001 | - | |
|
| 0.6596 | 19200 | 0.0002 | - | |
|
| 0.6613 | 19250 | 0.0002 | - | |
|
| 0.6630 | 19300 | 0.0003 | - | |
|
| 0.6648 | 19350 | 0.0186 | - | |
|
| 0.6665 | 19400 | 0.0001 | - | |
|
| 0.6682 | 19450 | 0.0002 | - | |
|
| 0.6699 | 19500 | 0.0002 | - | |
|
| 0.6716 | 19550 | 0.0001 | - | |
|
| 0.6734 | 19600 | 0.0001 | - | |
|
| 0.6751 | 19650 | 0.0001 | - | |
|
| 0.6768 | 19700 | 0.0001 | - | |
|
| 0.6785 | 19750 | 0.0001 | - | |
|
| 0.6802 | 19800 | 0.0001 | - | |
|
| 0.6819 | 19850 | 0.0001 | - | |
|
| 0.6837 | 19900 | 0.0001 | - | |
|
| 0.6854 | 19950 | 0.0371 | - | |
|
| 0.6871 | 20000 | 0.0001 | - | |
|
| 0.6888 | 20050 | 0.0001 | - | |
|
| 0.6905 | 20100 | 0.0001 | - | |
|
| 0.6922 | 20150 | 0.0001 | - | |
|
| 0.6940 | 20200 | 0.0001 | - | |
|
| 0.6957 | 20250 | 0.0001 | - | |
|
| 0.6974 | 20300 | 0.0001 | - | |
|
| 0.6991 | 20350 | 0.0001 | - | |
|
| 0.7008 | 20400 | 0.0001 | - | |
|
| 0.7026 | 20450 | 0.0001 | - | |
|
| 0.7043 | 20500 | 0.0002 | - | |
|
| 0.7060 | 20550 | 0.0001 | - | |
|
| 0.7077 | 20600 | 0.0002 | - | |
|
| 0.7094 | 20650 | 0.0001 | - | |
|
| 0.7111 | 20700 | 0.0001 | - | |
|
| 0.7129 | 20750 | 0.0001 | - | |
|
| 0.7146 | 20800 | 0.0001 | - | |
|
| 0.7163 | 20850 | 0.0001 | - | |
|
| 0.7180 | 20900 | 0.0001 | - | |
|
| 0.7197 | 20950 | 0.0001 | - | |
|
| 0.7215 | 21000 | 0.0001 | - | |
|
| 0.7232 | 21050 | 0.0001 | - | |
|
| 0.7249 | 21100 | 0.0363 | - | |
|
| 0.7266 | 21150 | 0.0001 | - | |
|
| 0.7283 | 21200 | 0.0001 | - | |
|
| 0.7300 | 21250 | 0.0001 | - | |
|
| 0.7318 | 21300 | 0.0001 | - | |
|
| 0.7335 | 21350 | 0.0001 | - | |
|
| 0.7352 | 21400 | 0.0001 | - | |
|
| 0.7369 | 21450 | 0.0001 | - | |
|
| 0.7386 | 21500 | 0.0001 | - | |
|
| 0.7403 | 21550 | 0.0001 | - | |
|
| 0.7421 | 21600 | 0.0001 | - | |
|
| 0.7438 | 21650 | 0.0001 | - | |
|
| 0.7455 | 21700 | 0.0001 | - | |
|
| 0.7472 | 21750 | 0.0001 | - | |
|
| 0.7489 | 21800 | 0.0001 | - | |
|
| 0.7507 | 21850 | 0.0001 | - | |
|
| 0.7524 | 21900 | 0.0001 | - | |
|
| 0.7541 | 21950 | 0.0001 | - | |
|
| 0.7558 | 22000 | 0.0001 | - | |
|
| 0.7575 | 22050 | 0.0358 | - | |
|
| 0.7592 | 22100 | 0.0007 | - | |
|
| 0.7610 | 22150 | 0.0001 | - | |
|
| 0.7627 | 22200 | 0.0001 | - | |
|
| 0.7644 | 22250 | 0.0001 | - | |
|
| 0.7661 | 22300 | 0.0001 | - | |
|
| 0.7678 | 22350 | 0.0001 | - | |
|
| 0.7695 | 22400 | 0.0368 | - | |
|
| 0.7713 | 22450 | 0.0001 | - | |
|
| 0.7730 | 22500 | 0.0001 | - | |
|
| 0.7747 | 22550 | 0.0001 | - | |
|
| 0.7764 | 22600 | 0.0001 | - | |
|
| 0.7781 | 22650 | 0.0001 | - | |
|
| 0.7799 | 22700 | 0.0003 | - | |
|
| 0.7816 | 22750 | 0.0001 | - | |
|
| 0.7833 | 22800 | 0.0001 | - | |
|
| 0.7850 | 22850 | 0.0001 | - | |
|
| 0.7867 | 22900 | 0.0001 | - | |
|
| 0.7884 | 22950 | 0.0001 | - | |
|
| 0.7902 | 23000 | 0.0001 | - | |
|
| 0.7919 | 23050 | 0.0001 | - | |
|
| 0.7936 | 23100 | 0.0001 | - | |
|
| 0.7953 | 23150 | 0.0001 | - | |
|
| 0.7970 | 23200 | 0.0001 | - | |
|
| 0.7987 | 23250 | 0.0001 | - | |
|
| 0.8005 | 23300 | 0.0001 | - | |
|
| 0.8022 | 23350 | 0.0001 | - | |
|
| 0.8039 | 23400 | 0.0002 | - | |
|
| 0.8056 | 23450 | 0.0001 | - | |
|
| 0.8073 | 23500 | 0.0001 | - | |
|
| 0.8091 | 23550 | 0.0001 | - | |
|
| 0.8108 | 23600 | 0.0001 | - | |
|
| 0.8125 | 23650 | 0.0001 | - | |
|
| 0.8142 | 23700 | 0.0173 | - | |
|
| 0.8159 | 23750 | 0.0001 | - | |
|
| 0.8176 | 23800 | 0.0001 | - | |
|
| 0.8194 | 23850 | 0.0001 | - | |
|
| 0.8211 | 23900 | 0.0001 | - | |
|
| 0.8228 | 23950 | 0.0001 | - | |
|
| 0.8245 | 24000 | 0.0001 | - | |
|
| 0.8262 | 24050 | 0.0001 | - | |
|
| 0.8280 | 24100 | 0.0001 | - | |
|
| 0.8297 | 24150 | 0.0001 | - | |
|
| 0.8314 | 24200 | 0.0001 | - | |
|
| 0.8331 | 24250 | 0.0001 | - | |
|
| 0.8348 | 24300 | 0.0001 | - | |
|
| 0.8365 | 24350 | 0.0001 | - | |
|
| 0.8383 | 24400 | 0.0001 | - | |
|
| 0.8400 | 24450 | 0.0001 | - | |
|
| 0.8417 | 24500 | 0.0003 | - | |
|
| 0.8434 | 24550 | 0.0002 | - | |
|
| 0.8451 | 24600 | 0.0002 | - | |
|
| 0.8468 | 24650 | 0.0001 | - | |
|
| 0.8486 | 24700 | 0.0001 | - | |
|
| 0.8503 | 24750 | 0.0001 | - | |
|
| 0.8520 | 24800 | 0.0004 | - | |
|
| 0.8537 | 24850 | 0.0001 | - | |
|
| 0.8554 | 24900 | 0.0001 | - | |
|
| 0.8572 | 24950 | 0.0001 | - | |
|
| 0.8589 | 25000 | 0.0001 | - | |
|
| 0.8606 | 25050 | 0.0001 | - | |
|
| 0.8623 | 25100 | 0.0372 | - | |
|
| 0.8640 | 25150 | 0.0001 | - | |
|
| 0.8657 | 25200 | 0.0001 | - | |
|
| 0.8675 | 25250 | 0.0001 | - | |
|
| 0.8692 | 25300 | 0.0001 | - | |
|
| 0.8709 | 25350 | 0.0001 | - | |
|
| 0.8726 | 25400 | 0.0001 | - | |
|
| 0.8743 | 25450 | 0.0001 | - | |
|
| 0.8760 | 25500 | 0.0001 | - | |
|
| 0.8778 | 25550 | 0.0002 | - | |
|
| 0.8795 | 25600 | 0.0001 | - | |
|
| 0.8812 | 25650 | 0.0001 | - | |
|
| 0.8829 | 25700 | 0.0001 | - | |
|
| 0.8846 | 25750 | 0.0001 | - | |
|
| 0.8864 | 25800 | 0.0001 | - | |
|
| 0.8881 | 25850 | 0.0001 | - | |
|
| 0.8898 | 25900 | 0.0001 | - | |
|
| 0.8915 | 25950 | 0.0001 | - | |
|
| 0.8932 | 26000 | 0.0001 | - | |
|
| 0.8949 | 26050 | 0.0001 | - | |
|
| 0.8967 | 26100 | 0.0001 | - | |
|
| 0.8984 | 26150 | 0.0001 | - | |
|
| 0.9001 | 26200 | 0.0001 | - | |
|
| 0.9018 | 26250 | 0.0001 | - | |
|
| 0.9035 | 26300 | 0.0001 | - | |
|
| 0.9052 | 26350 | 0.0001 | - | |
|
| 0.9070 | 26400 | 0.0001 | - | |
|
| 0.9087 | 26450 | 0.0001 | - | |
|
| 0.9104 | 26500 | 0.0001 | - | |
|
| 0.9121 | 26550 | 0.0001 | - | |
|
| 0.9138 | 26600 | 0.0001 | - | |
|
| 0.9156 | 26650 | 0.0001 | - | |
|
| 0.9173 | 26700 | 0.0001 | - | |
|
| 0.9190 | 26750 | 0.0001 | - | |
|
| 0.9207 | 26800 | 0.0001 | - | |
|
| 0.9224 | 26850 | 0.0001 | - | |
|
| 0.9241 | 26900 | 0.0001 | - | |
|
| 0.9259 | 26950 | 0.0001 | - | |
|
| 0.9276 | 27000 | 0.0001 | - | |
|
| 0.9293 | 27050 | 0.0001 | - | |
|
| 0.9310 | 27100 | 0.0001 | - | |
|
| 0.9327 | 27150 | 0.0001 | - | |
|
| 0.9345 | 27200 | 0.0001 | - | |
|
| 0.9362 | 27250 | 0.0001 | - | |
|
| 0.9379 | 27300 | 0.0001 | - | |
|
| 0.9396 | 27350 | 0.0001 | - | |
|
| 0.9413 | 27400 | 0.0001 | - | |
|
| 0.9430 | 27450 | 0.0001 | - | |
|
| 0.9448 | 27500 | 0.0001 | - | |
|
| 0.9465 | 27550 | 0.0001 | - | |
|
| 0.9482 | 27600 | 0.0001 | - | |
|
| 0.9499 | 27650 | 0.0001 | - | |
|
| 0.9516 | 27700 | 0.0001 | - | |
|
| 0.9533 | 27750 | 0.0001 | - | |
|
| 0.9551 | 27800 | 0.0001 | - | |
|
| 0.9568 | 27850 | 0.0001 | - | |
|
| 0.9585 | 27900 | 0.0001 | - | |
|
| 0.9602 | 27950 | 0.0001 | - | |
|
| 0.9619 | 28000 | 0.0001 | - | |
|
| 0.9637 | 28050 | 0.0001 | - | |
|
| 0.9654 | 28100 | 0.0001 | - | |
|
| 0.9671 | 28150 | 0.0001 | - | |
|
| 0.9688 | 28200 | 0.0001 | - | |
|
| 0.9705 | 28250 | 0.0001 | - | |
|
| 0.9722 | 28300 | 0.0001 | - | |
|
| 0.9740 | 28350 | 0.0001 | - | |
|
| 0.9757 | 28400 | 0.0001 | - | |
|
| 0.9774 | 28450 | 0.0001 | - | |
|
| 0.9791 | 28500 | 0.0001 | - | |
|
| 0.9808 | 28550 | 0.0001 | - | |
|
| 0.9825 | 28600 | 0.0001 | - | |
|
| 0.9843 | 28650 | 0.0001 | - | |
|
| 0.9860 | 28700 | 0.0001 | - | |
|
| 0.9877 | 28750 | 0.0001 | - | |
|
| 0.9894 | 28800 | 0.0001 | - | |
|
| 0.9911 | 28850 | 0.0001 | - | |
|
| 0.9929 | 28900 | 0.0001 | - | |
|
| 0.9946 | 28950 | 0.0001 | - | |
|
| 0.9963 | 29000 | 0.0001 | - | |
|
| 0.9980 | 29050 | 0.0374 | - | |
|
| 0.9997 | 29100 | 0.0001 | - | |
|
|
|
### Framework Versions |
|
- Python: 3.10.13 |
|
- SetFit: 1.0.3 |
|
- Sentence Transformers: 2.6.1 |
|
- Transformers: 4.38.2 |
|
- PyTorch: 2.1.2 |
|
- Datasets: 2.17.0 |
|
- Tokenizers: 0.15.2 |
|
|
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## Citation |
|
|
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### BibTeX |
|
```bibtex |
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@article{https://doi.org/10.48550/arxiv.2209.11055, |
|
doi = {10.48550/ARXIV.2209.11055}, |
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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}, |
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year = {2022}, |
|
copyright = {Creative Commons Attribution 4.0 International} |
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} |
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``` |
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