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Wispra-fr
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setfit-url-classifier

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
text-embeddings-inference
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Upload url-classifier-v1 (accuracy: 0.888)

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Files changed (13) hide show
  1. 1_Pooling/config.json +9 -0
  2. README.md +545 -3
  3. config.json +26 -0
  4. config_sentence_transformers.json +7 -0
  5. config_setfit.json +14 -0
  6. model.safetensors +3 -0
  7. model_head.pkl +3 -0
  8. modules.json +14 -0
  9. sentence_bert_config.json +4 -0
  10. special_tokens_map.json +37 -0
  11. tokenizer.json +0 -0
  12. tokenizer_config.json +57 -0
  13. vocab.txt +0 -0
1_Pooling/config.json ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "word_embedding_dimension": 384,
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+ "pooling_mode_cls_token": false,
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+ "pooling_mode_mean_tokens": true,
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+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_mean_sqrt_len_tokens": false,
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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false
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+ }
README.md CHANGED
@@ -1,3 +1,545 @@
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- ---
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- license: mit
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: https://lesjupesdeprune.com/pages/contact
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+ - text: https://www.ebspaca.fr/news-standard/
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+ - text: https://www.pepejeans.com/fr_fr/enfants/fille
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+ - text: https://ynspir.com
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+ - text: https://www.bivouack.fr/
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+ pipeline_tag: text-classification
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+ inference: true
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+ base_model: sentence-transformers/paraphrase-MiniLM-L6-v2
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+ ---
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+
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+ # SetFit with sentence-transformers/paraphrase-MiniLM-L6-v2
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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. This SetFit model uses [sentence-transformers/paraphrase-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L6-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
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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 body:** [sentence-transformers/paraphrase-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L6-v2)
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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:** 9 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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+ | navigation | <ul><li>'https://www.groupe-ocea.fr'</li><li>'https://woemen.fr/'</li><li>'https://fourchenoire.com/fr'</li></ul> |
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+ | product | <ul><li>'https://www.woemen.fr/products/tshirt-oversize-mixte-blanc'</li><li>'https://www.bivouack.fr/salon-sejour/lit-escamotable-canape/ensemble-lit-escamotable-horizontal-avec-canape-et-bureau-citala'</li><li>'https://www.bivouack.fr/lit-escamotable-vertical-nida'</li></ul> |
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+ | service | <ul><li>'https://pub-pro.fr/service/decorations-adhesives-depolies/'</li><li>'https://bclimatisation.fr/installation-chauffage-rennes/'</li><li>'https://www.philartstudio.com/mariages/reportage/noelie-marc/'</li></ul> |
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+ | brand | <ul><li>'https://ynspir.com/guide-marques/orac-decor/'</li><li>'https://cogitech.fr/en/studio-2/'</li><li>'https://woemen.fr/pages/woemen-x-lubia-partenariat-protections-periodiques'</li></ul> |
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+ | legal | <ul><li>'https://ditesmoioui.com/faqs/'</li><li>'https://www.scal-ia.fr/contact'</li><li>'https://pub-pro.fr/contact'</li></ul> |
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+ | listing | <ul><li>'https://www.pepejeans.com/fr_fr/femme/accessoires/lunettes-de-soleil'</li><li>'https://ynspir.com/conseils-decoration/agencement/amenagement-studio/'</li><li>'https://www.ebspaca.fr/service-carousel/'</li></ul> |
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+ | post | <ul><li>'https://woemen.fr/blogs/le-coton-biologique'</li><li>'https://ynspir.com/divers/quelles-couleurs-choisir-pour-un-interieur-lumineux-et-chaleureux/'</li><li>'https://www.lacompagnieducaviar.fr/post/nos-idées-de-recettes-comment-sublimer-vos-plats-avec-nos-produits-autour-du-caviar'</li></ul> |
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+ | testimonial | <ul><li>'https://cogitech.fr/en/realisation/design-en/epee/'</li><li>'https://cogitech.fr/realisation/design/dynamic-landscape-fr/'</li><li>'https://cogitech.fr/en/realisation/design-en/apollo-2/'</li></ul> |
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+ | account_shop | <ul><li>'https://www.pepejeans.com/fr_fr/jeans-fit-guide-man.html'</li><li>'https://www.woemen.fr/policies/refund-policy'</li><li>'https://lesjupesdeprune.com/customer_authentication/login'</li></ul> |
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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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+
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+ First install the SetFit library:
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+
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+ ```bash
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+ pip install setfit
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+ ```
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+
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+ 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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+
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+ # Download from the 🤗 Hub
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+ model = SetFitModel.from_pretrained("Wispra-fr/setfit-url-classifier")
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+ # Run inference
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+ preds = model("https://ynspir.com")
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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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+
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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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+ <!--
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+ ## Bias, Risks and Limitations
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+
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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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+ <!--
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+ ### Recommendations
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+
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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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+
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+ ## Training Details
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+
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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 | 1 | 1.0 | 1 |
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+
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+ | Label | Training Sample Count |
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+ |:-------------|:----------------------|
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+ | legal | 70 |
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+ | brand | 86 |
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+ | product | 176 |
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+ | service | 184 |
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+ | post | 133 |
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+ | navigation | 53 |
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+ | listing | 265 |
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+ | account_shop | 38 |
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+ | testimonial | 132 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (32, 32)
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+ - num_epochs: (5, 5)
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+ - max_steps: -1
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+ - sampling_strategy: undersampling
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+ - num_iterations: 10
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+ - body_learning_rate: (2e-05, 2e-05)
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+ - head_learning_rate: 2e-05
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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: False
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+ - use_amp: True
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+ - warmup_proportion: 0.1
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+ - max_length: 128
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+ - seed: 42
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+ - eval_max_steps: -1
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+ - load_best_model_at_end: False
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+
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+ ### Training Results
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+ | Epoch | Step | Training Loss | Validation Loss |
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+ |:------:|:----:|:-------------:|:---------------:|
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+ | 0.0014 | 1 | 0.2637 | - |
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+ | 0.0141 | 10 | 0.2127 | - |
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+ | 0.0281 | 20 | 0.2137 | - |
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+ | 0.0422 | 30 | 0.2431 | - |
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+ | 0.0563 | 40 | 0.2347 | - |
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+ | 0.0703 | 50 | 0.2264 | - |
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+ | 0.0844 | 60 | 0.2391 | - |
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+ | 0.0985 | 70 | 0.2395 | - |
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+ | 0.1125 | 80 | 0.253 | - |
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+ | 0.1266 | 90 | 0.2295 | - |
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+ | 0.1406 | 100 | 0.2281 | - |
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+ | 0.1547 | 110 | 0.2216 | - |
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+ | 0.1688 | 120 | 0.2297 | - |
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+ | 0.1828 | 130 | 0.2471 | - |
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+ | 0.1969 | 140 | 0.209 | - |
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+ | 0.2110 | 150 | 0.1998 | - |
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+ | 0.2250 | 160 | 0.1932 | - |
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+ | 0.2391 | 170 | 0.1821 | - |
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+ | 0.2532 | 180 | 0.1894 | - |
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+ | 0.2672 | 190 | 0.1685 | - |
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+ | 0.2813 | 200 | 0.163 | - |
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+ | 0.2954 | 210 | 0.2206 | - |
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+ | 0.3094 | 220 | 0.1698 | - |
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+ | 0.3235 | 230 | 0.2171 | - |
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+ | 0.3376 | 240 | 0.1834 | - |
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+ | 0.3516 | 250 | 0.1678 | - |
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+ | 0.3657 | 260 | 0.1537 | - |
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+ | 0.3797 | 270 | 0.1527 | - |
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+ | 0.3938 | 280 | 0.1751 | - |
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+ | 0.4079 | 290 | 0.152 | - |
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+ | 0.4219 | 300 | 0.1371 | - |
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+ | 0.4360 | 310 | 0.1403 | - |
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+ | 0.4501 | 320 | 0.1042 | - |
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+ | 0.4641 | 330 | 0.1108 | - |
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+ | 0.4782 | 340 | 0.1003 | - |
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+ | 0.4923 | 350 | 0.1226 | - |
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+ | 0.5063 | 360 | 0.1613 | - |
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+ | 0.5204 | 370 | 0.1259 | - |
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+ | 0.5345 | 380 | 0.0877 | - |
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+ | 0.5485 | 390 | 0.1067 | - |
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+ | 0.5626 | 400 | 0.1143 | - |
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+ | 0.5767 | 410 | 0.096 | - |
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+ | 0.5907 | 420 | 0.0557 | - |
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+ | 0.6048 | 430 | 0.051 | - |
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+ | 0.6188 | 440 | 0.1339 | - |
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+ | 0.6329 | 450 | 0.0846 | - |
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+ | 0.6470 | 460 | 0.0657 | - |
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+ | 0.6610 | 470 | 0.0812 | - |
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+ | 0.6751 | 480 | 0.058 | - |
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+ | 0.6892 | 490 | 0.093 | - |
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+ | 0.7032 | 500 | 0.0397 | - |
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+ | 0.7173 | 510 | 0.0932 | - |
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+ | 0.7314 | 520 | 0.062 | - |
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+ | 0.7454 | 530 | 0.0595 | - |
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+ | 0.7595 | 540 | 0.0774 | - |
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+ | 0.7736 | 550 | 0.0444 | - |
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+ | 0.7876 | 560 | 0.06 | - |
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+ | 0.8017 | 570 | 0.0486 | - |
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+ | 0.8158 | 580 | 0.0708 | - |
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+ | 0.8298 | 590 | 0.0518 | - |
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+ | 0.8439 | 600 | 0.0479 | - |
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+ | 0.8579 | 610 | 0.0511 | - |
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+ | 0.8720 | 620 | 0.0722 | - |
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+ | 0.8861 | 630 | 0.0691 | - |
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+ | 0.9001 | 640 | 0.0841 | - |
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+ | 0.9142 | 650 | 0.0997 | - |
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+ | 0.9283 | 660 | 0.0583 | - |
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+ | 0.9423 | 670 | 0.0264 | - |
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+ | 0.9564 | 680 | 0.0452 | - |
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+ | 0.9705 | 690 | 0.0174 | - |
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+ | 0.9845 | 700 | 0.0448 | - |
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+ | 0.9986 | 710 | 0.043 | - |
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+ | 1.0127 | 720 | 0.0801 | - |
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+ | 1.0267 | 730 | 0.063 | - |
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+ | 1.0408 | 740 | 0.0376 | - |
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+ | 1.0549 | 750 | 0.0273 | - |
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+ | 1.0689 | 760 | 0.0516 | - |
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+ | 1.0830 | 770 | 0.0317 | - |
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+ | 1.0970 | 780 | 0.0247 | - |
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+ | 1.1111 | 790 | 0.0254 | - |
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+ | 1.1252 | 800 | 0.0213 | - |
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+ | 1.1392 | 810 | 0.0172 | - |
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+ | 1.1533 | 820 | 0.0365 | - |
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+ | 1.1674 | 830 | 0.0247 | - |
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+ | 1.1814 | 840 | 0.0471 | - |
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+ | 1.1955 | 850 | 0.0248 | - |
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+ | 1.2096 | 860 | 0.0711 | - |
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+ | 1.2236 | 870 | 0.0231 | - |
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+ | 1.2377 | 880 | 0.0504 | - |
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+ | 1.2518 | 890 | 0.0477 | - |
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+ | 1.2658 | 900 | 0.0104 | - |
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+ | 1.2799 | 910 | 0.0338 | - |
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+ | 1.2940 | 920 | 0.0116 | - |
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+ | 1.3080 | 930 | 0.0599 | - |
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+ | 1.3221 | 940 | 0.0215 | - |
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+ | 1.3361 | 950 | 0.0418 | - |
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+ | 1.3502 | 960 | 0.0265 | - |
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+ | 1.3643 | 970 | 0.0371 | - |
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+ | 1.3783 | 980 | 0.0334 | - |
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+ | 1.3924 | 990 | 0.0478 | - |
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+ | 1.4065 | 1000 | 0.0223 | - |
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+ | 1.4205 | 1010 | 0.0132 | - |
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+ | 1.4346 | 1020 | 0.0166 | - |
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+ | 1.4487 | 1030 | 0.035 | - |
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+ | 1.4627 | 1040 | 0.0081 | - |
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+ | 1.4768 | 1050 | 0.0238 | - |
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+ | 1.4909 | 1060 | 0.0177 | - |
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+ | 1.5049 | 1070 | 0.009 | - |
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+ | 1.5190 | 1080 | 0.0296 | - |
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+ | 1.5331 | 1090 | 0.0323 | - |
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+ | 1.5471 | 1100 | 0.0237 | - |
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+ | 1.5612 | 1110 | 0.0298 | - |
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+ | 1.5752 | 1120 | 0.0592 | - |
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+ | 1.5893 | 1130 | 0.0052 | - |
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+ | 1.6034 | 1140 | 0.0112 | - |
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+ | 1.6174 | 1150 | 0.0477 | - |
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+ | 1.6315 | 1160 | 0.0356 | - |
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+ | 1.6456 | 1170 | 0.0324 | - |
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+ | 1.6596 | 1180 | 0.0412 | - |
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+ | 1.6737 | 1190 | 0.0484 | - |
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+ | 1.6878 | 1200 | 0.0262 | - |
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+ | 1.7018 | 1210 | 0.011 | - |
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+ | 1.7159 | 1220 | 0.0075 | - |
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+ | 1.7300 | 1230 | 0.0471 | - |
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+ | 1.7440 | 1240 | 0.0398 | - |
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+ | 1.7581 | 1250 | 0.0335 | - |
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+ | 1.7722 | 1260 | 0.0278 | - |
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+ | 1.7862 | 1270 | 0.0533 | - |
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+ | 1.8003 | 1280 | 0.0291 | - |
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+ | 1.8143 | 1290 | 0.0122 | - |
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+ | 1.8284 | 1300 | 0.0039 | - |
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+ | 1.8425 | 1310 | 0.0043 | - |
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+ | 1.8565 | 1320 | 0.0135 | - |
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+ | 1.8706 | 1330 | 0.0182 | - |
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+ | 1.8847 | 1340 | 0.0306 | - |
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+ | 1.8987 | 1350 | 0.0135 | - |
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+ | 1.9128 | 1360 | 0.0034 | - |
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+ | 1.9269 | 1370 | 0.0109 | - |
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+ | 1.9409 | 1380 | 0.0209 | - |
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+ | 1.9550 | 1390 | 0.0244 | - |
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+ | 1.9691 | 1400 | 0.0052 | - |
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+ | 1.9831 | 1410 | 0.0095 | - |
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+ | 1.9972 | 1420 | 0.0067 | - |
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+ | 2.0113 | 1430 | 0.0091 | - |
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+ | 2.0253 | 1440 | 0.0077 | - |
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+ | 2.0394 | 1450 | 0.0246 | - |
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+ | 2.0534 | 1460 | 0.0123 | - |
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+ | 2.0675 | 1470 | 0.0061 | - |
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+ | 2.0816 | 1480 | 0.0375 | - |
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+ | 2.0956 | 1490 | 0.0187 | - |
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+ | 2.1097 | 1500 | 0.0029 | - |
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+ | 2.1238 | 1510 | 0.0043 | - |
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+ | 2.1378 | 1520 | 0.0191 | - |
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+ | 2.1519 | 1530 | 0.0039 | - |
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+ | 2.1660 | 1540 | 0.0628 | - |
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+ | 2.1800 | 1550 | 0.0278 | - |
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+ | 2.1941 | 1560 | 0.0106 | - |
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+ | 2.2082 | 1570 | 0.0192 | - |
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+ | 2.2222 | 1580 | 0.0127 | - |
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+ | 2.2363 | 1590 | 0.0053 | - |
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+ | 2.2504 | 1600 | 0.0211 | - |
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+ | 2.2644 | 1610 | 0.0291 | - |
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+ | 2.2785 | 1620 | 0.0043 | - |
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+ | 2.2925 | 1630 | 0.0147 | - |
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+ | 2.3066 | 1640 | 0.0219 | - |
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+ | 2.3207 | 1650 | 0.0017 | - |
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+ | 2.3347 | 1660 | 0.0114 | - |
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+ | 2.3488 | 1670 | 0.0056 | - |
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+ | 2.3629 | 1680 | 0.0075 | - |
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+ | 2.3769 | 1690 | 0.0191 | - |
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+ | 2.3910 | 1700 | 0.0049 | - |
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+ | 2.4051 | 1710 | 0.0279 | - |
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+ | 2.4191 | 1720 | 0.0081 | - |
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+ | 2.4332 | 1730 | 0.0047 | - |
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+ | 2.4473 | 1740 | 0.0035 | - |
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+ | 2.4613 | 1750 | 0.0024 | - |
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+ | 2.4754 | 1760 | 0.0022 | - |
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+ | 2.4895 | 1770 | 0.0091 | - |
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+ | 2.5035 | 1780 | 0.0238 | - |
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+ | 2.5176 | 1790 | 0.0084 | - |
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+ | 2.5316 | 1800 | 0.0267 | - |
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+ | 2.5457 | 1810 | 0.0071 | - |
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+ | 2.5598 | 1820 | 0.0027 | - |
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+ | 2.5738 | 1830 | 0.0226 | - |
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+ | 2.5879 | 1840 | 0.0032 | - |
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+ | 2.6020 | 1850 | 0.0014 | - |
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+ | 2.6160 | 1860 | 0.0028 | - |
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+ | 2.6301 | 1870 | 0.0043 | - |
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+ | 2.6442 | 1880 | 0.0105 | - |
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+ | 2.6582 | 1890 | 0.0036 | - |
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+ | 2.6723 | 1900 | 0.0031 | - |
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+ | 2.6864 | 1910 | 0.008 | - |
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+ | 2.7004 | 1920 | 0.0296 | - |
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+ | 2.7145 | 1930 | 0.0103 | - |
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+ | 2.7286 | 1940 | 0.0234 | - |
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+ | 2.7426 | 1950 | 0.0035 | - |
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+ | 2.7567 | 1960 | 0.0252 | - |
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+ | 2.7707 | 1970 | 0.0238 | - |
345
+ | 2.7848 | 1980 | 0.0045 | - |
346
+ | 2.7989 | 1990 | 0.0304 | - |
347
+ | 2.8129 | 2000 | 0.0021 | - |
348
+ | 2.8270 | 2010 | 0.0046 | - |
349
+ | 2.8411 | 2020 | 0.0027 | - |
350
+ | 2.8551 | 2030 | 0.0169 | - |
351
+ | 2.8692 | 2040 | 0.0089 | - |
352
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353
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354
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355
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356
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357
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358
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359
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360
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361
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362
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363
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364
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365
+ | 3.0661 | 2180 | 0.0017 | - |
366
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367
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368
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369
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370
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371
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373
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+
504
+ ### Framework Versions
505
+ - Python: 3.11.0rc1
506
+ - SetFit: 1.0.3
507
+ - Sentence Transformers: 2.3.1
508
+ - Transformers: 4.40.2
509
+ - PyTorch: 2.1.2+cu121
510
+ - Datasets: 2.16.1
511
+ - Tokenizers: 0.19.1
512
+
513
+ ## Citation
514
+
515
+ ### BibTeX
516
+ ```bibtex
517
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
518
+ doi = {10.48550/ARXIV.2209.11055},
519
+ url = {https://arxiv.org/abs/2209.11055},
520
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
521
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
522
+ title = {Efficient Few-Shot Learning Without Prompts},
523
+ publisher = {arXiv},
524
+ year = {2022},
525
+ copyright = {Creative Commons Attribution 4.0 International}
526
+ }
527
+ ```
528
+
529
+ <!--
530
+ ## Glossary
531
+
532
+ *Clearly define terms in order to be accessible across audiences.*
533
+ -->
534
+
535
+ <!--
536
+ ## Model Card Authors
537
+
538
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
539
+ -->
540
+
541
+ <!--
542
+ ## Model Card Contact
543
+
544
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
545
+ -->
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