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rafi138
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setfit-paraphrase-mpnet-base-v2-business-type

Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Eval Results
Model card Files Community
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Add SetFit model

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Files changed (13) hide show
  1. 1_Pooling/config.json +7 -0
  2. README.md +896 -0
  3. config.json +24 -0
  4. config_sentence_transformers.json +7 -0
  5. config_setfit.json +6 -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 +51 -0
  11. tokenizer.json +0 -0
  12. tokenizer_config.json +66 -0
  13. vocab.txt +0 -0
1_Pooling/config.json ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
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+ {
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+ "word_embedding_dimension": 768,
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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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+ }
README.md ADDED
@@ -0,0 +1,896 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: Dadon Hotel
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+ - text: Joyi Homeo Hall
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+ - text: Masum Egg Supplier
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+ - text: Salam Automobiles
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+ - text: Shoumik Enterprise
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+ pipeline_tag: text-classification
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+ inference: true
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+ base_model: sentence-transformers/paraphrase-mpnet-base-v2
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+ model-index:
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+ - name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
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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: 0.57
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+ name: Accuracy
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+ ---
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+
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+ # SetFit with sentence-transformers/paraphrase-mpnet-base-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-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-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-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-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:** 512 tokens
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+ - **Number of Classes:** 19 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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+ | Utility | <ul><li>'Pole No 198'</li><li>'Mirpur 12/A Harun Mollah Eidgah Math Water Pump'</li><li>'Pole No 44'</li></ul> |
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+ | Education | <ul><li>'Jamirs Care'</li><li>'Institute Of Marine Technology Chandpur'</li><li>'Nobo Digonto Coaching Center'</li></ul> |
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+ | Office | <ul><li>'Media And Multimedia'</li><li>'Ataur Rahman Atiq Lawfarm'</li><li>'Kemiko Pharmaceutical Limited'</li></ul> |
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+ | Commercial | <ul><li>'Lakshmipur Police Line Shopping Mall'</li><li>'Bolaka Building'</li><li>'Vegetables Market'</li></ul> |
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+ | Industry | <ul><li>'Haque Food Industries Limited'</li><li>'Agig Poultry Firm'</li><li>'Regular Washing Plant'</li></ul> |
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+ | Healthcare | <ul><li>'Utsob Homeo Hall'</li><li>'Homeo Biochemic Chikitsa Kendro'</li><li>'Ananta Homeo Clinic'</li></ul> |
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+ | Residential | <ul><li>'Hotel City Garden'</li><li>'Kasmi Bhaban'</li><li>'AR Tower'</li></ul> |
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+ | Government | <ul><li>'Cyclone Preparedness Program Ministry of Disaster Management and Relief'</li><li>'Fire Service And Civil Defense'</li><li>'Sherpur Bwdb'</li></ul> |
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+ | Hotel | <ul><li>'China Inn Limited'</li><li>'Hotel Salimar International'</li><li>'Hotel Grand Surma'</li></ul> |
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+ | Religious Place | <ul><li>'Soudagor Tola Hazrat Gorom Dhawan (R.) Jame Masjid'</li><li>'Shah Makhdum (R:) Masjid'</li><li>'Mahiganj Central Graveyard'</li></ul> |
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+ | Food | <ul><li>'Takwaya Biryani House'</li><li>'Jilapi Ghor'</li><li>'Sad Biryani House'</li></ul> |
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+ | Fuel | <ul><li>'A N Enterprise'</li><li>'Sabbir Fuel Station'</li><li>'Trade Consortium'</li></ul> |
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+ | Agricultural | <ul><li>'Vasha Sainik Samir Ahmed Bohumukhi Khamar'</li><li>'Tanzila Nursery'</li><li>'Insaf Agro'</li></ul> |
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+ | Construction | <ul><li>'Bohurupa Enterprise'</li><li>'M/S Jamiya Steel House'</li><li>'Nure Madina Timber And Saw Mill'</li></ul> |
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+ | Recreation | <ul><li>'Bijoygatha Community Center'</li><li>'Shahid Doctor Fazle Rabbi Park'</li><li>'Kalabagan Staff Quarter Field'</li></ul> |
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+ | Shop | <ul><li>'Tawfiq Confectionery & Varieties 2'</li><li>'Tammi store'</li><li>'Shah Jalal Timber Merchant & Sawmill'</li></ul> |
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+ | Transportation | <ul><li>'Sagorika Paribahan'</li><li>'Bismillah Ahmed Transport Agency'</li><li>'Medda Bus Stand'</li></ul> |
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+ | Bank | <ul><li>'United Commercial Bank Jatrabari (UCB)'</li><li>'Union Bank Limited Dewan Bazar Branch'</li><li>'Janata Bank Badda'</li></ul> |
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+ | Landmark | <ul><li>'Dilkhola Moar'</li><li>'Gouripur Motlob Road Moar'</li><li>'Bot Chattar'</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** | 0.57 |
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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("rafi138/setfit-paraphrase-mpnet-base-v2-business-type")
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+ # Run inference
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+ preds = model("Dadon Hotel")
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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 | 3.5132 | 11 |
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+
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+ | Label | Training Sample Count |
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+ |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------|
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+ | ShopCommercialGovernmentHealthcareEducationFoodOfficeReligious PlaceBankTransportationConstructionIndustryResidentialLandmarkRecreationFuelHotelUtilityAgricultural | 0 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (16, 16)
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+ - num_epochs: (4, 4)
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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: False
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+ - use_amp: False
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+ - warmup_proportion: 0.1
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+ - seed: 42
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+ - eval_max_steps: -1
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+ - load_best_model_at_end: True
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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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+ | 0.0001 | 1 | 0.2213 | - |
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+ | 0.0058 | 50 | 0.253 | - |
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+ | 0.0117 | 100 | 0.2571 | - |
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+ | 0.0175 | 150 | 0.2189 | - |
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+ | 0.0234 | 200 | 0.2138 | - |
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+ | 0.0292 | 250 | 0.173 | - |
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+ | 0.0351 | 300 | 0.2221 | - |
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+ | 0.0409 | 350 | 0.1669 | - |
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+ | 0.0468 | 400 | 0.1214 | - |
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+ | 0.0526 | 450 | 0.18 | - |
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+ | 0.0585 | 500 | 0.0983 | - |
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+ | 0.0643 | 550 | 0.0965 | - |
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+ | 0.0702 | 600 | 0.0819 | - |
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+ | 0.0760 | 650 | 0.1476 | - |
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+ | 0.0819 | 700 | 0.1289 | - |
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+ | 0.0877 | 750 | 0.0926 | - |
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+ | 0.0936 | 800 | 0.1395 | - |
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+ | 0.0994 | 850 | 0.1124 | - |
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+ | 0.1053 | 900 | 0.1089 | - |
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+ | 0.1111 | 950 | 0.1349 | - |
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+ | 0.1170 | 1000 | 0.0884 | - |
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+ | 0.1228 | 1050 | 0.0559 | - |
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+ | 0.1287 | 1100 | 0.0433 | - |
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+ | 0.1345 | 1150 | 0.0556 | - |
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+ | 0.1404 | 1200 | 0.081 | - |
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+ | 0.1462 | 1250 | 0.0334 | - |
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+ | 0.1520 | 1300 | 0.0659 | - |
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+ | 0.1579 | 1350 | 0.0103 | - |
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+ | 0.1637 | 1400 | 0.0638 | - |
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+ | 0.1696 | 1450 | 0.0298 | - |
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+ | 0.1754 | 1500 | 0.0496 | - |
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+ | 0.1813 | 1550 | 0.0114 | - |
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+ | 0.1871 | 1600 | 0.023 | - |
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+ | 0.1930 | 1650 | 0.0613 | - |
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+ | 0.1988 | 1700 | 0.0098 | - |
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+ | 0.2047 | 1750 | 0.0141 | - |
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+ | 0.2105 | 1800 | 0.0034 | - |
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+ | 0.2164 | 1850 | 0.0095 | - |
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+ | 0.2222 | 1900 | 0.005 | - |
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+ | 0.2281 | 1950 | 0.0034 | - |
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+ | 0.2339 | 2000 | 0.051 | - |
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+ | 0.2398 | 2050 | 0.0038 | - |
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+ | 0.2456 | 2100 | 0.0091 | - |
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+ | 0.2515 | 2150 | 0.0027 | - |
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+ | 0.2573 | 2200 | 0.003 | - |
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+ | 0.2632 | 2250 | 0.0014 | - |
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+ | 0.2690 | 2300 | 0.0032 | - |
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+ | 0.2749 | 2350 | 0.0731 | - |
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+ | 0.2807 | 2400 | 0.0025 | - |
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+ | 0.2865 | 2450 | 0.0041 | - |
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+ | 0.2924 | 2500 | 0.0061 | - |
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+ | 0.2982 | 2550 | 0.0016 | - |
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+ | 0.3041 | 2600 | 0.0027 | - |
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+ | 0.3099 | 2650 | 0.002 | - |
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+ | 0.3158 | 2700 | 0.0013 | - |
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+ | 0.3216 | 2750 | 0.0155 | - |
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+ | 0.3275 | 2800 | 0.0079 | - |
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+ | 0.3333 | 2850 | 0.0026 | - |
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+ | 0.3392 | 2900 | 0.001 | - |
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+ | 0.3450 | 2950 | 0.0024 | - |
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+ | 0.3509 | 3000 | 0.0015 | - |
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+ | 0.3567 | 3050 | 0.0006 | - |
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+ | 0.3626 | 3100 | 0.0072 | - |
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+ | 0.3684 | 3150 | 0.0023 | - |
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+ | 0.3743 | 3200 | 0.001 | - |
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+ | 0.3801 | 3250 | 0.0011 | - |
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+ | 0.3860 | 3300 | 0.0126 | - |
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+ | 0.3918 | 3350 | 0.0025 | - |
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+ | 0.3977 | 3400 | 0.0009 | - |
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+ | 0.4035 | 3450 | 0.006 | - |
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+ | 0.4094 | 3500 | 0.0011 | - |
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+ | 0.4152 | 3550 | 0.001 | - |
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+ | 0.4211 | 3600 | 0.0017 | - |
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+ | 0.4269 | 3650 | 0.0009 | - |
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+ | 0.4327 | 3700 | 0.0007 | - |
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+ | 0.4386 | 3750 | 0.0006 | - |
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+ | 0.4444 | 3800 | 0.0007 | - |
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+ | 0.4503 | 3850 | 0.0007 | - |
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+ | 0.4561 | 3900 | 0.0005 | - |
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+ | 0.4620 | 3950 | 0.0006 | - |
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+ | 0.4678 | 4000 | 0.0005 | - |
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+ | 0.4737 | 4050 | 0.0003 | - |
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+ | 0.4795 | 4100 | 0.0003 | - |
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+ | 0.4854 | 4150 | 0.0003 | - |
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+ | 0.4912 | 4200 | 0.0003 | - |
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+ | 0.4971 | 4250 | 0.0013 | - |
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+ | 0.5029 | 4300 | 0.0009 | - |
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+ | 0.5088 | 4350 | 0.0003 | - |
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+ | 0.5146 | 4400 | 0.0007 | - |
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+ | 0.5205 | 4450 | 0.0006 | - |
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+ | 0.5263 | 4500 | 0.0005 | - |
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+ | 0.5322 | 4550 | 0.0005 | - |
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+ | 0.5380 | 4600 | 0.0006 | - |
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+ | 0.5439 | 4650 | 0.0005 | - |
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+ | 0.5497 | 4700 | 0.0004 | - |
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+ | 0.5556 | 4750 | 0.0004 | - |
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+ | 0.5614 | 4800 | 0.0003 | - |
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+ | 0.5673 | 4850 | 0.0003 | - |
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+ | 0.5731 | 4900 | 0.0005 | - |
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+ | 0.5789 | 4950 | 0.0005 | - |
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+ | 0.5848 | 5000 | 0.0008 | - |
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+ | 0.5906 | 5050 | 0.0003 | - |
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+ | 0.5965 | 5100 | 0.0004 | - |
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+ | 0.6023 | 5150 | 0.0003 | - |
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+ | 0.6082 | 5200 | 0.0004 | - |
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+ | 0.6140 | 5250 | 0.0003 | - |
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+ | 0.6199 | 5300 | 0.0003 | - |
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+ | 0.6257 | 5350 | 0.0004 | - |
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+ | 0.6316 | 5400 | 0.0003 | - |
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+ | 0.6374 | 5450 | 0.0003 | - |
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+ | 0.6433 | 5500 | 0.0002 | - |
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+ | 0.6491 | 5550 | 0.0003 | - |
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+ | 0.6550 | 5600 | 0.0003 | - |
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+ | 0.6608 | 5650 | 0.0008 | - |
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+ | 0.6667 | 5700 | 0.0003 | - |
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+ | 0.6725 | 5750 | 0.0004 | - |
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+ | 0.6784 | 5800 | 0.0007 | - |
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+ | 0.6842 | 5850 | 0.0372 | - |
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+ | 0.6901 | 5900 | 0.0045 | - |
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+ | 0.6959 | 5950 | 0.0041 | - |
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+ | 0.7018 | 6000 | 0.0006 | - |
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+ | 0.7076 | 6050 | 0.0004 | - |
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+ | 0.7135 | 6100 | 0.0005 | - |
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+ | 0.7193 | 6150 | 0.0003 | - |
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+ | 0.7251 | 6200 | 0.0002 | - |
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+ | 0.7310 | 6250 | 0.0022 | - |
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+ | 0.7368 | 6300 | 0.0004 | - |
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+ | 0.7427 | 6350 | 0.0003 | - |
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+ | 0.7485 | 6400 | 0.0003 | - |
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+ | 0.7544 | 6450 | 0.0002 | - |
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+ | 0.7602 | 6500 | 0.0004 | - |
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+ | 0.7661 | 6550 | 0.0006 | - |
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+ | 0.7719 | 6600 | 0.0002 | - |
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+ | 0.7778 | 6650 | 0.0003 | - |
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+ | 0.7836 | 6700 | 0.0002 | - |
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+ | 0.7895 | 6750 | 0.0002 | - |
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+ | 0.7953 | 6800 | 0.0003 | - |
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+ | 0.8012 | 6850 | 0.0003 | - |
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+ | 0.8070 | 6900 | 0.0003 | - |
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+ | 0.8129 | 6950 | 0.0007 | - |
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+ | 0.8187 | 7000 | 0.0002 | - |
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+ | 0.8246 | 7050 | 0.0002 | - |
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+ | 0.8304 | 7100 | 0.0002 | - |
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+ | 0.8363 | 7150 | 0.0002 | - |
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+ | 0.8421 | 7200 | 0.0003 | - |
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+ | 0.8480 | 7250 | 0.0002 | - |
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+ | 0.8538 | 7300 | 0.0002 | - |
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+ | 0.8596 | 7350 | 0.0002 | - |
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+ | 0.8655 | 7400 | 0.0002 | - |
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+ | 0.8713 | 7450 | 0.0003 | - |
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+ | 0.8772 | 7500 | 0.0001 | - |
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+ | 0.8830 | 7550 | 0.0001 | - |
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+ | 0.8889 | 7600 | 0.0002 | - |
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+ | 0.8947 | 7650 | 0.0002 | - |
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+ | 0.9006 | 7700 | 0.0002 | - |
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+ | 0.9064 | 7750 | 0.0002 | - |
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+ | 0.9123 | 7800 | 0.0002 | - |
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+ | 0.9181 | 7850 | 0.0001 | - |
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+ | 0.9240 | 7900 | 0.0002 | - |
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+ | 0.9298 | 7950 | 0.0001 | - |
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+ | 0.9357 | 8000 | 0.0003 | - |
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+ | 0.9415 | 8050 | 0.0001 | - |
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+ | 0.9474 | 8100 | 0.0002 | - |
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+ | 0.9532 | 8150 | 0.0001 | - |
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+ | 0.9591 | 8200 | 0.0001 | - |
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+ | 0.9649 | 8250 | 0.0001 | - |
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+ | 0.9708 | 8300 | 0.0001 | - |
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+ | 0.9766 | 8350 | 0.0002 | - |
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+ | 0.9825 | 8400 | 0.0002 | - |
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+ | 0.9883 | 8450 | 0.0001 | - |
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+ | 0.9942 | 8500 | 0.0001 | - |
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+ | 1.0 | 8550 | 0.0002 | 0.3616 |
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+ | 1.0058 | 8600 | 0.0003 | - |
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+ | 1.0117 | 8650 | 0.0002 | - |
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+ | 1.0175 | 8700 | 0.0002 | - |
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+ | 1.0234 | 8750 | 0.0002 | - |
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+ | 1.0292 | 8800 | 0.0001 | - |
345
+ | 1.0351 | 8850 | 0.0001 | - |
346
+ | 1.0409 | 8900 | 0.0001 | - |
347
+ | 1.0468 | 8950 | 0.0002 | - |
348
+ | 1.0526 | 9000 | 0.0002 | - |
349
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350
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351
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352
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353
+ | 1.0819 | 9250 | 0.0002 | - |
354
+ | 1.0877 | 9300 | 0.0002 | - |
355
+ | 1.0936 | 9350 | 0.0002 | - |
356
+ | 1.0994 | 9400 | 0.0002 | - |
357
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358
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359
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360
+ | 1.1228 | 9600 | 0.0001 | - |
361
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362
+ | 1.1345 | 9700 | 0.0001 | - |
363
+ | 1.1404 | 9750 | 0.0002 | - |
364
+ | 1.1462 | 9800 | 0.0004 | - |
365
+ | 1.1520 | 9850 | 0.0367 | - |
366
+ | 1.1579 | 9900 | 0.0009 | - |
367
+ | 1.1637 | 9950 | 0.038 | - |
368
+ | 1.1696 | 10000 | 0.0005 | - |
369
+ | 1.1754 | 10050 | 0.0005 | - |
370
+ | 1.1813 | 10100 | 0.0004 | - |
371
+ | 1.1871 | 10150 | 0.0002 | - |
372
+ | 1.1930 | 10200 | 0.0002 | - |
373
+ | 1.1988 | 10250 | 0.0002 | - |
374
+ | 1.2047 | 10300 | 0.0002 | - |
375
+ | 1.2105 | 10350 | 0.0002 | - |
376
+ | 1.2164 | 10400 | 0.0002 | - |
377
+ | 1.2222 | 10450 | 0.0001 | - |
378
+ | 1.2281 | 10500 | 0.0003 | - |
379
+ | 1.2339 | 10550 | 0.0002 | - |
380
+ | 1.2398 | 10600 | 0.0002 | - |
381
+ | 1.2456 | 10650 | 0.0003 | - |
382
+ | 1.2515 | 10700 | 0.0002 | - |
383
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384
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385
+ | 1.2690 | 10850 | 0.0002 | - |
386
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387
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388
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389
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390
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391
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392
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393
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394
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395
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396
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397
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398
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399
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400
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401
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402
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403
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404
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405
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406
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407
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408
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409
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410
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411
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412
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413
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414
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415
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416
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417
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418
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419
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420
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421
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422
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423
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424
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425
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426
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427
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428
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429
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430
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431
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432
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433
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434
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435
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436
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437
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438
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439
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440
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441
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442
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443
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444
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445
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446
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447
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448
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449
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450
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451
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452
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453
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454
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455
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456
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457
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458
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459
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460
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461
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462
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463
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464
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465
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466
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467
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468
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469
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470
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471
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472
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473
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474
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475
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476
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477
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478
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479
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480
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481
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482
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483
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484
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485
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486
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487
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488
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489
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490
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491
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492
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493
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494
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495
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496
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497
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498
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499
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500
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501
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502
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503
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504
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505
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506
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507
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508
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509
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510
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511
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512
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513
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514
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515
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516
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517
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518
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519
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520
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521
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522
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523
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524
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525
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526
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527
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528
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529
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530
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531
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532
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533
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534
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535
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536
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537
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538
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539
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540
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541
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542
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543
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544
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545
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546
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547
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548
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549
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550
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551
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552
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553
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554
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555
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556
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557
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558
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559
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560
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561
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562
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563
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564
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565
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566
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567
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568
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569
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570
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571
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572
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573
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574
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575
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576
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577
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578
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579
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580
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581
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582
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583
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584
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585
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586
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587
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588
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589
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590
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591
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592
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593
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594
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595
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596
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597
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598
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599
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600
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601
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602
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603
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604
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605
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606
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607
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608
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609
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610
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611
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612
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613
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614
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615
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616
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617
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618
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619
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620
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621
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622
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623
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624
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625
+ | 2.6725 | 22850 | 0.0001 | - |
626
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627
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628
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629
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630
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631
+ | 2.7076 | 23150 | 0.0001 | - |
632
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633
+ | 2.7193 | 23250 | 0.0001 | - |
634
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635
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636
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637
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638
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639
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640
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641
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642
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643
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644
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645
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646
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647
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648
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649
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650
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651
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652
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653
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654
+ | 2.8421 | 24300 | 0.0001 | - |
655
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656
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657
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658
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659
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660
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661
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662
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663
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664
+ | 2.9006 | 24800 | 0.0001 | - |
665
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666
+ | 2.9123 | 24900 | 0.0001 | - |
667
+ | 2.9181 | 24950 | 0.0001 | - |
668
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669
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670
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671
+ | 2.9415 | 25150 | 0.0001 | - |
672
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673
+ | 2.9532 | 25250 | 0.0001 | - |
674
+ | 2.9591 | 25300 | 0.0001 | - |
675
+ | 2.9649 | 25350 | 0.0001 | - |
676
+ | 2.9708 | 25400 | 0.0001 | - |
677
+ | 2.9766 | 25450 | 0.0001 | - |
678
+ | 2.9825 | 25500 | 0.0001 | - |
679
+ | 2.9883 | 25550 | 0.0001 | - |
680
+ | 2.9942 | 25600 | 0.0001 | - |
681
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682
+ | 3.0058 | 25700 | 0.0001 | - |
683
+ | 3.0117 | 25750 | 0.0001 | - |
684
+ | 3.0175 | 25800 | 0.0001 | - |
685
+ | 3.0234 | 25850 | 0.0001 | - |
686
+ | 3.0292 | 25900 | 0.0001 | - |
687
+ | 3.0351 | 25950 | 0.0001 | - |
688
+ | 3.0409 | 26000 | 0.0001 | - |
689
+ | 3.0468 | 26050 | 0.0001 | - |
690
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691
+ | 3.0585 | 26150 | 0.0001 | - |
692
+ | 3.0643 | 26200 | 0.0001 | - |
693
+ | 3.0702 | 26250 | 0.0001 | - |
694
+ | 3.0760 | 26300 | 0.0001 | - |
695
+ | 3.0819 | 26350 | 0.0001 | - |
696
+ | 3.0877 | 26400 | 0.0001 | - |
697
+ | 3.0936 | 26450 | 0.0001 | - |
698
+ | 3.0994 | 26500 | 0.0001 | - |
699
+ | 3.1053 | 26550 | 0.0001 | - |
700
+ | 3.1111 | 26600 | 0.0001 | - |
701
+ | 3.1170 | 26650 | 0.0001 | - |
702
+ | 3.1228 | 26700 | 0.0001 | - |
703
+ | 3.1287 | 26750 | 0.0001 | - |
704
+ | 3.1345 | 26800 | 0.0001 | - |
705
+ | 3.1404 | 26850 | 0.0001 | - |
706
+ | 3.1462 | 26900 | 0.0001 | - |
707
+ | 3.1520 | 26950 | 0.0001 | - |
708
+ | 3.1579 | 27000 | 0.0001 | - |
709
+ | 3.1637 | 27050 | 0.0001 | - |
710
+ | 3.1696 | 27100 | 0.0001 | - |
711
+ | 3.1754 | 27150 | 0.0001 | - |
712
+ | 3.1813 | 27200 | 0.0001 | - |
713
+ | 3.1871 | 27250 | 0.0001 | - |
714
+ | 3.1930 | 27300 | 0.0001 | - |
715
+ | 3.1988 | 27350 | 0.0001 | - |
716
+ | 3.2047 | 27400 | 0.0001 | - |
717
+ | 3.2105 | 27450 | 0.0001 | - |
718
+ | 3.2164 | 27500 | 0.0001 | - |
719
+ | 3.2222 | 27550 | 0.0001 | - |
720
+ | 3.2281 | 27600 | 0.0001 | - |
721
+ | 3.2339 | 27650 | 0.0001 | - |
722
+ | 3.2398 | 27700 | 0.0001 | - |
723
+ | 3.2456 | 27750 | 0.0001 | - |
724
+ | 3.2515 | 27800 | 0.0001 | - |
725
+ | 3.2573 | 27850 | 0.0001 | - |
726
+ | 3.2632 | 27900 | 0.0001 | - |
727
+ | 3.2690 | 27950 | 0.0001 | - |
728
+ | 3.2749 | 28000 | 0.0001 | - |
729
+ | 3.2807 | 28050 | 0.0001 | - |
730
+ | 3.2865 | 28100 | 0.0001 | - |
731
+ | 3.2924 | 28150 | 0.0001 | - |
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+
854
+ * The bold row denotes the saved checkpoint.
855
+ ### Framework Versions
856
+ - Python: 3.10.12
857
+ - SetFit: 1.0.3
858
+ - Sentence Transformers: 2.2.2
859
+ - Transformers: 4.36.2
860
+ - PyTorch: 2.1.2+cu121
861
+ - Datasets: 2.16.1
862
+ - Tokenizers: 0.15.0
863
+
864
+ ## Citation
865
+
866
+ ### BibTeX
867
+ ```bibtex
868
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
869
+ doi = {10.48550/ARXIV.2209.11055},
870
+ url = {https://arxiv.org/abs/2209.11055},
871
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
872
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
873
+ title = {Efficient Few-Shot Learning Without Prompts},
874
+ publisher = {arXiv},
875
+ year = {2022},
876
+ copyright = {Creative Commons Attribution 4.0 International}
877
+ }
878
+ ```
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+
880
+ <!--
881
+ ## Glossary
882
+
883
+ *Clearly define terms in order to be accessible across audiences.*
884
+ -->
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+
886
+ <!--
887
+ ## Model Card Authors
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+
889
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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+ -->
891
+
892
+ <!--
893
+ ## Model Card Contact
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+
895
+ *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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