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kaustubhgap
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kaustubh_setfit

Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Model card Files
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Files changed (13) hide show
  1. 1_Pooling/config.json +7 -0
  2. README.md +880 -0
  3. config.json +24 -0
  4. config_sentence_transformers.json +7 -0
  5. config_setfit.json +4 -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 +59 -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 +1,880 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: one piece
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+ - text: tube
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+ - text: heavy weight
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+ - text: track
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+ - text: unitard
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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: 0.5762331838565022
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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:** 119 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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+ | 79 | <ul><li>'peony middle notes'</li><li>'lemon middle notes'</li><li>'coconut middle notes'</li></ul> |
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+ | 86 | <ul><li>'no print/no pattern'</li><li>'two tone'</li><li>'diagonal stripe'</li></ul> |
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+ | 37 | <ul><li>'eel skin leather'</li><li>'metal'</li><li>'raffia'</li></ul> |
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+ | 82 | <ul><li>'collarless'</li><li>'peaked lapel'</li><li>'front keyhole'</li></ul> |
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+ | 95 | <ul><li>'standard toe'</li><li>'wide toe'</li><li>'extra wide toe'</li></ul> |
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+ | 83 | <ul><li>'indoor'</li><li>'hike'</li><li>'beach'</li></ul> |
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+ | 107 | <ul><li>'surplice'</li><li>'messenger bag'</li><li>'camera bag'</li></ul> |
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+ | 19 | <ul><li>'mary jane'</li><li>'zip around wallet'</li><li>'tongue buckle'</li></ul> |
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+ | 102 | <ul><li>'slits at knee'</li><li>'slits above hips'</li><li>'front slit at hem'</li></ul> |
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+ | 35 | <ul><li>'tie'</li><li>'gem embellishment'</li><li>'caged'</li></ul> |
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+ | 18 | <ul><li>'rolo chain'</li><li>'cord bracelet'</li><li>'figaro'</li></ul> |
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+ | 65 | <ul><li>'wheat protein'</li><li>'rosemary ingredient'</li><li>'pea protein'</li></ul> |
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+ | 68 | <ul><li>'bath towel'</li><li>'art print'</li><li>'reusable bottle'</li></ul> |
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+ | 40 | <ul><li>'polyfill'</li><li>'silk fill'</li><li>'feather fill'</li></ul> |
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+ | 50 | <ul><li>'palm grip'</li><li>'carpenter hook'</li><li>'storm flap'</li></ul> |
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+ | 113 | <ul><li>'wide waistband'</li><li>'elastic inset'</li><li>'belt loops'</li></ul> |
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+ | 75 | <ul><li>'glass'</li><li>'acrylic'</li><li>'opal'</li></ul> |
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+ | 11 | <ul><li>'foam cups'</li><li>'wire'</li><li>'molded cups'</li></ul> |
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+ | 38 | <ul><li>'dual layer fabric'</li><li>'2 way stretch'</li><li>'4 way stretch'</li></ul> |
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+ | 63 | <ul><li>'light support'</li><li>'medium supprt'</li><li>'high support'</li></ul> |
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+ | 44 | <ul><li>'face'</li><li>'hand'</li><li>'neck/dècolletage'</li></ul> |
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+ | 115 | <ul><li>'soy wax'</li><li>'paraffin wax'</li></ul> |
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+ | 42 | <ul><li>'regular'</li><li>'tailored'</li><li>'fitted'</li></ul> |
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+ | 97 | <ul><li>'king'</li><li>'euro'</li><li>'standard'</li></ul> |
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+ | 70 | <ul><li>'wrist length'</li><li>'above thigh'</li><li>'below bust'</li></ul> |
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+ | 34 | <ul><li>'feminine'</li><li>'religious'</li><li>'boho'</li></ul> |
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+ | 10 | <ul><li>'slim'</li><li>'regular'</li></ul> |
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+ | 15 | <ul><li>'6-10 oz'</li><li>'11-20 oz'</li></ul> |
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+ | 77 | <ul><li>'rose gold metal'</li><li>'gold plated'</li><li>'alloy'</li></ul> |
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+ | 43 | <ul><li>'contrast inner lining'</li><li>'simple seaming'</li><li>'princess seams'</li></ul> |
95
+ | 7 | <ul><li>'neroli base notes'</li><li>'amber base notes'</li><li>'musk base notes'</li></ul> |
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+ | 17 | <ul><li>'spot clean'</li><li>'dry clean'</li><li>'microwave safe'</li></ul> |
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+ | 8 | <ul><li>'nourishing'</li><li>'firming'</li><li>'soothing/healing'</li></ul> |
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+ | 103 | <ul><li>'lugged soles'</li><li>'non marking soles'</li></ul> |
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+ | 26 | <ul><li>'wall control'</li><li>'switch control'</li></ul> |
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+ | 99 | <ul><li>'fitted sleeves'</li><li>'fitted sleeve'</li><li>'structured sleeves'</li></ul> |
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+ | 33 | <ul><li>'rim'</li><li>'feet'</li><li>'5 panel construction'</li></ul> |
102
+ | 64 | <ul><li>'mineral oil free'</li><li>'propylene glycol free'</li><li>'paraffin free'</li></ul> |
103
+ | 96 | <ul><li>'double strap'</li><li>'spaghetti straps'</li><li>'thin straps'</li></ul> |
104
+ | 1 | <ul><li>'shoulder back'</li><li>'full coverage'</li><li>'low back'</li></ul> |
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+ | 62 | <ul><li>'rustic'</li><li>'coastal'</li><li>'scandinavian'</li></ul> |
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+ | 39 | <ul><li>'metallic'</li><li>'swiss dot'</li><li>'base layer'</li></ul> |
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+ | 60 | <ul><li>'halloween'</li><li>'christmas holiday'</li></ul> |
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+ | 92 | <ul><li>'seamless'</li><li>'mid rise waist seam'</li><li>'flat seam'</li></ul> |
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+ | 114 | <ul><li>'ultra high rise'</li><li>'mid rise'</li><li>'high waisted'</li></ul> |
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+ | 105 | <ul><li>'top handle'</li><li>'detachable straps'</li><li>'chain strap'</li></ul> |
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+ | 90 | <ul><li>'floral'</li><li>'psychedelic print'</li><li>'paisley'</li></ul> |
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+ | 91 | <ul><li>'night'</li><li>'day'</li></ul> |
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+ | 45 | <ul><li>'serum formulation'</li><li>'cream/creme'</li><li>'solid'</li></ul> |
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+ | 59 | <ul><li>'strong hold'</li><li>'flexible hold'</li></ul> |
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+ | 46 | <ul><li>'leather'</li><li>'fresh aquatic'</li><li>'green aromatic'</li></ul> |
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+ | 21 | <ul><li>'matte'</li><li>'metallic'</li><li>'olive'</li></ul> |
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+ | 69 | <ul><li>'cinnamon key notes'</li><li>'violet key notes'</li><li>'pepper key notes'</li></ul> |
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+ | 101 | <ul><li>'dropped shoulder'</li><li>'puff shoulder'</li><li>'flutter sleeve'</li></ul> |
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+ | 61 | <ul><li>'summer'</li><li>'everyday'</li><li>'indoor'</li></ul> |
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+ | 104 | <ul><li>'wedding guest'</li><li>'bridal'</li><li>'halloween'</li></ul> |
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+ | 32 | <ul><li>'indigo wash'</li><li>'acid wash'</li><li>'stonewash'</li></ul> |
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+ | 51 | <ul><li>'still life graphic'</li><li>'sports graphic'</li><li>'star wars'</li></ul> |
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+ | 48 | <ul><li>'beige'</li><li>'black'</li><li>'rose gold frame'</li></ul> |
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+ | 87 | <ul><li>'medium pile'</li><li>'low pile'</li></ul> |
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+ | 22 | <ul><li>'bright'</li><li>'pastel'</li><li>'light'</li></ul> |
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+ | 41 | <ul><li>'matte finish'</li><li>'shiny finish'</li></ul> |
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+ | 93 | <ul><li>'no buckle'</li><li>'geometric shape'</li><li>'straight silhouette'</li></ul> |
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+ | 71 | <ul><li>'polarized'</li><li>'color tinted'</li><li>'mirrored'</li></ul> |
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+ | 2 | <ul><li>'split back'</li><li>'racer back'</li><li>'open back'</li></ul> |
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+ | 89 | <ul><li>'round stitch pocket'</li><li>'seam pocket'</li><li>'kangaroo pocket'</li></ul> |
131
+ | 20 | <ul><li>'removable hoodie'</li><li>'packable hood collar'</li><li>'hooded'</li></ul> |
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+ | 52 | <ul><li>'thick'</li><li>'medium thick'</li></ul> |
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+ | 55 | <ul><li>'amber head notes'</li><li>'lime head notes'</li><li>'musk head notes'</li></ul> |
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+ | 58 | <ul><li>'back curved hem'</li><li>'twist hem'</li><li>'ribbed hem'</li></ul> |
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+ | 118 | <ul><li>'light wood'</li><li>'medium wood'</li></ul> |
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+ | 25 | <ul><li>'gifts for him'</li><li>'apres ski'</li><li>'cozy'</li></ul> |
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+ | 109 | <ul><li>'closed toe'</li><li>'square toe'</li><li>'round toe'</li></ul> |
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+ | 30 | <ul><li>'extended cuffs'</li><li>'storm cuffs'</li><li>'elastic cuff'</li></ul> |
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+ | 24 | <ul><li>'ingrown hairs'</li><li>'frizz'</li><li>'redness'</li></ul> |
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+ | 9 | <ul><li>'high cut'</li><li>'string bikini'</li></ul> |
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+ | 94 | <ul><li>'opaque'</li><li>'sheer'</li></ul> |
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+ | 16 | <ul><li>'2 card slot'</li><li>'card slots'</li></ul> |
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+ | 78 | <ul><li>'gothcore'</li><li>'vanilla girl'</li><li>'dyed out'</li></ul> |
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+ | 4 | <ul><li>'layered'</li><li>'bangle'</li><li>'cuff'</li></ul> |
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+ | 23 | <ul><li>'parfum'</li><li>'eau de toilette'</li></ul> |
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+ | 111 | <ul><li>'delicate'</li><li>'statement'</li></ul> |
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+ | 12 | <ul><li>'flat brim'</li><li>'curved brim'</li><li>'fold over brim'</li></ul> |
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+ | 98 | <ul><li>'dry'</li><li>'acne prone'</li><li>'mature'</li></ul> |
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+ | 57 | <ul><li>'stacked heel'</li><li>'kitten heel'</li><li>'cone heel'</li></ul> |
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+ | 67 | <ul><li>'id slot'</li><li>'interior pocket'</li><li>'interior zipper pocket'</li></ul> |
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+ | 31 | <ul><li>'light wash'</li><li>'medium wash'</li><li>'colored'</li></ul> |
152
+ | 85 | <ul><li>'detailed stitching pant'</li><li>'simple seaming'</li></ul> |
153
+ | 116 | <ul><li>'knotted'</li><li>'percale'</li><li>'waffle weave'</li></ul> |
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+ | 88 | <ul><li>'shag'</li><li>'cut pile'</li></ul> |
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+ | 74 | <ul><li>'study hall'</li><li>'y2k'</li><li>'enchanted'</li></ul> |
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+ | 72 | <ul><li>'fur'</li><li>'fleece'</li><li>'mesh'</li></ul> |
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+ | 108 | <ul><li>'animal'</li><li>'love'</li></ul> |
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+ | 73 | <ul><li>'unlined'</li><li>'fully lined'</li><li>'partially lined'</li></ul> |
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+ | 13 | <ul><li>'wide brim'</li><li>'medium brim'</li></ul> |
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+ | 76 | <ul><li>'bpa free material'</li><li>'scratch resistant material'</li></ul> |
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+ | 54 | <ul><li>'straight handle'</li><li>'curved handle'</li></ul> |
162
+ | 100 | <ul><li>'rolled up sleeves'</li><li>'3/4 sleeve'</li><li>'bracelet length'</li></ul> |
163
+ | 84 | <ul><li>'manual open'</li><li>'auto open'</li></ul> |
164
+ | 14 | <ul><li>'wide'</li><li>'medium'</li></ul> |
165
+ | 27 | <ul><li>'superhero'</li><li>'disney'</li></ul> |
166
+ | 49 | <ul><li>'half rim'</li><li>'full rim'</li></ul> |
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+ | 29 | <ul><li>'tall crown'</li><li>'short crown'</li></ul> |
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+ | 106 | <ul><li>'low stretch'</li><li>'non stretch'</li></ul> |
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+ | 112 | <ul><li>'mid vamp'</li><li>'high vamp'</li></ul> |
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+ | 66 | <ul><li>'large interior'</li><li>'medium interior'</li><li>'small interior'</li></ul> |
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+ | 53 | <ul><li>'all hair types'</li><li>'damaged/dry hair'</li></ul> |
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+ | 117 | <ul><li>'light weight'</li><li>'mid weight'</li></ul> |
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+ | 81 | <ul><li>'low cut'</li><li>'mid chest neckline'</li><li>'open front'</li></ul> |
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+ | 5 | <ul><li>'thin band'</li><li>'soft band elastic'</li><li>'elastic band'</li></ul> |
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+ | 28 | <ul><li>'flat top crown'</li><li>'round crown'</li><li>'no crown'</li></ul> |
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+ | 56 | <ul><li>'ultra high heel'</li><li>'mid heel'</li><li>'high heel'</li></ul> |
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+ | 110 | <ul><li>'relaxed'</li><li>'tailored'</li></ul> |
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+ | 47 | <ul><li>'uplifting'</li><li>'bold'</li></ul> |
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+ | 3 | <ul><li>'changing pad'</li><li>'bottle pocket'</li></ul> |
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+ | 0 | <ul><li>'squeeze dispenser'</li><li>'dropper'</li></ul> |
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+ | 80 | <ul><li>'wall mount'</li><li>'ceiling mount'</li></ul> |
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+ | 6 | <ul><li>'medium'</li><li>'wide'</li></ul> |
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+ | 36 | <ul><li>'exterior pocket'</li><li>'exterior snap pocket'</li></ul> |
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+
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+ ## Evaluation
186
+
187
+ ### Metrics
188
+ | Label | Accuracy |
189
+ |:--------|:---------|
190
+ | **all** | 0.5762 |
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+
192
+ ## Uses
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+
194
+ ### Direct Use for Inference
195
+
196
+ First install the SetFit library:
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+
198
+ ```bash
199
+ pip install setfit
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+ ```
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+
202
+ Then you can load this model and run inference.
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+
204
+ ```python
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+ from setfit import SetFitModel
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+
207
+ # Download from the 🤗 Hub
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+ model = SetFitModel.from_pretrained("kaustubhgap/kaustubh_setfit")
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+ # Run inference
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+ preds = model("tube")
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+ ```
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+
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+ <!--
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+ ### Downstream Use
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+
216
+ *List how someone could finetune this model on their own dataset.*
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+ -->
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+
219
+ <!--
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+ ### Out-of-Scope Use
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+
222
+ *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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+
225
+ <!--
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+ ## Bias, Risks and Limitations
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+
228
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
229
+ -->
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+
231
+ <!--
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+ ### Recommendations
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+
234
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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+ -->
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+
237
+ ## Training Details
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+
239
+ ### Training Set Metrics
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+ | Training set | Min | Median | Max |
241
+ |:-------------|:----|:-------|:----|
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+ | Word count | 1 | 1.7047 | 6 |
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+
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+ | Label | Training Sample Count |
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+ |:------|:----------------------|
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+ | 0 | 2 |
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+ | 1 | 5 |
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+ | 2 | 12 |
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+ | 3 | 2 |
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+ | 4 | 6 |
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+ | 5 | 3 |
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+ | 6 | 2 |
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+ | 7 | 12 |
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+ | 8 | 16 |
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+ | 9 | 2 |
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+ | 10 | 2 |
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+ | 11 | 11 |
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+ | 12 | 4 |
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+ | 13 | 2 |
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+ | 14 | 2 |
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+ | 15 | 2 |
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+ | 16 | 2 |
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+ | 17 | 6 |
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+ | 18 | 9 |
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+ | 19 | 63 |
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+ | 20 | 8 |
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+ | 21 | 31 |
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+ | 22 | 6 |
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+ | 23 | 2 |
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+ | 24 | 13 |
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+ | 25 | 5 |
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+ | 26 | 2 |
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+ | 27 | 2 |
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+ | 28 | 3 |
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+ | 29 | 2 |
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+ | 30 | 13 |
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+ | 31 | 3 |
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+ | 32 | 7 |
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+ | 33 | 22 |
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+ | 34 | 12 |
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+ | 35 | 102 |
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+ | 36 | 2 |
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+ | 37 | 119 |
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+ | 38 | 34 |
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+ | 39 | 32 |
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+ | 40 | 6 |
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+ | 41 | 2 |
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+ | 42 | 13 |
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+ | 43 | 17 |
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+ | 44 | 5 |
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+ | 45 | 10 |
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+ | 46 | 6 |
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+ | 47 | 2 |
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+ | 48 | 10 |
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+ | 49 | 2 |
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+ | 50 | 91 |
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+ | 51 | 13 |
298
+ | 52 | 2 |
299
+ | 53 | 2 |
300
+ | 54 | 2 |
301
+ | 55 | 12 |
302
+ | 56 | 4 |
303
+ | 57 | 7 |
304
+ | 58 | 17 |
305
+ | 59 | 2 |
306
+ | 60 | 2 |
307
+ | 61 | 7 |
308
+ | 62 | 9 |
309
+ | 63 | 3 |
310
+ | 64 | 14 |
311
+ | 65 | 53 |
312
+ | 66 | 3 |
313
+ | 67 | 6 |
314
+ | 68 | 41 |
315
+ | 69 | 41 |
316
+ | 70 | 33 |
317
+ | 71 | 5 |
318
+ | 72 | 5 |
319
+ | 73 | 4 |
320
+ | 74 | 7 |
321
+ | 75 | 49 |
322
+ | 76 | 2 |
323
+ | 77 | 23 |
324
+ | 78 | 11 |
325
+ | 79 | 12 |
326
+ | 80 | 2 |
327
+ | 81 | 5 |
328
+ | 82 | 33 |
329
+ | 83 | 33 |
330
+ | 84 | 2 |
331
+ | 85 | 2 |
332
+ | 86 | 17 |
333
+ | 87 | 2 |
334
+ | 88 | 2 |
335
+ | 89 | 10 |
336
+ | 90 | 29 |
337
+ | 91 | 2 |
338
+ | 92 | 8 |
339
+ | 93 | 21 |
340
+ | 94 | 2 |
341
+ | 95 | 3 |
342
+ | 96 | 5 |
343
+ | 97 | 10 |
344
+ | 98 | 5 |
345
+ | 99 | 6 |
346
+ | 100 | 6 |
347
+ | 101 | 12 |
348
+ | 102 | 13 |
349
+ | 103 | 2 |
350
+ | 104 | 10 |
351
+ | 105 | 28 |
352
+ | 106 | 2 |
353
+ | 107 | 321 |
354
+ | 108 | 2 |
355
+ | 109 | 10 |
356
+ | 110 | 2 |
357
+ | 111 | 2 |
358
+ | 112 | 2 |
359
+ | 113 | 15 |
360
+ | 114 | 4 |
361
+ | 115 | 2 |
362
+ | 116 | 5 |
363
+ | 117 | 2 |
364
+ | 118 | 2 |
365
+
366
+ ### Training Hyperparameters
367
+ - batch_size: (16, 16)
368
+ - num_epochs: (5, 5)
369
+ - max_steps: -1
370
+ - sampling_strategy: oversampling
371
+ - num_iterations: 20
372
+ - body_learning_rate: (2e-05, 1e-05)
373
+ - head_learning_rate: 0.01
374
+ - loss: CosineSimilarityLoss
375
+ - distance_metric: cosine_distance
376
+ - margin: 0.25
377
+ - end_to_end: False
378
+ - use_amp: False
379
+ - warmup_proportion: 0.1
380
+ - seed: 42
381
+ - eval_max_steps: -1
382
+ - load_best_model_at_end: False
383
+
384
+ ### Training Results
385
+ | Epoch | Step | Training Loss | Validation Loss |
386
+ |:------:|:-----:|:-------------:|:---------------:|
387
+ | 0.0002 | 1 | 0.2895 | - |
388
+ | 0.0112 | 50 | 0.2531 | - |
389
+ | 0.0225 | 100 | 0.2622 | - |
390
+ | 0.0337 | 150 | 0.2535 | - |
391
+ | 0.0449 | 200 | 0.2144 | - |
392
+ | 0.0561 | 250 | 0.206 | - |
393
+ | 0.0674 | 300 | 0.1583 | - |
394
+ | 0.0786 | 350 | 0.1384 | - |
395
+ | 0.0898 | 400 | 0.1778 | - |
396
+ | 0.1011 | 450 | 0.2111 | - |
397
+ | 0.1123 | 500 | 0.1791 | - |
398
+ | 0.1235 | 550 | 0.2198 | - |
399
+ | 0.1347 | 600 | 0.0918 | - |
400
+ | 0.1460 | 650 | 0.1027 | - |
401
+ | 0.1572 | 700 | 0.1837 | - |
402
+ | 0.1684 | 750 | 0.1762 | - |
403
+ | 0.1797 | 800 | 0.1552 | - |
404
+ | 0.1909 | 850 | 0.2045 | - |
405
+ | 0.2021 | 900 | 0.1338 | - |
406
+ | 0.2133 | 950 | 0.0495 | - |
407
+ | 0.2246 | 1000 | 0.1136 | - |
408
+ | 0.2358 | 1050 | 0.0878 | - |
409
+ | 0.2470 | 1100 | 0.1671 | - |
410
+ | 0.2583 | 1150 | 0.0791 | - |
411
+ | 0.2695 | 1200 | 0.1332 | - |
412
+ | 0.2807 | 1250 | 0.0712 | - |
413
+ | 0.2919 | 1300 | 0.1853 | - |
414
+ | 0.3032 | 1350 | 0.134 | - |
415
+ | 0.3144 | 1400 | 0.1123 | - |
416
+ | 0.3256 | 1450 | 0.0525 | - |
417
+ | 0.3369 | 1500 | 0.0901 | - |
418
+ | 0.3481 | 1550 | 0.1554 | - |
419
+ | 0.3593 | 1600 | 0.0417 | - |
420
+ | 0.3705 | 1650 | 0.0762 | - |
421
+ | 0.3818 | 1700 | 0.0155 | - |
422
+ | 0.3930 | 1750 | 0.0115 | - |
423
+ | 0.4042 | 1800 | 0.0665 | - |
424
+ | 0.4155 | 1850 | 0.0578 | - |
425
+ | 0.4267 | 1900 | 0.0271 | - |
426
+ | 0.4379 | 1950 | 0.1374 | - |
427
+ | 0.4491 | 2000 | 0.1125 | - |
428
+ | 0.4604 | 2050 | 0.0304 | - |
429
+ | 0.4716 | 2100 | 0.0636 | - |
430
+ | 0.4828 | 2150 | 0.0668 | - |
431
+ | 0.4940 | 2200 | 0.1055 | - |
432
+ | 0.5053 | 2250 | 0.1147 | - |
433
+ | 0.5165 | 2300 | 0.0358 | - |
434
+ | 0.5277 | 2350 | 0.1516 | - |
435
+ | 0.5390 | 2400 | 0.008 | - |
436
+ | 0.5502 | 2450 | 0.082 | - |
437
+ | 0.5614 | 2500 | 0.0937 | - |
438
+ | 0.5726 | 2550 | 0.1382 | - |
439
+ | 0.5839 | 2600 | 0.0527 | - |
440
+ | 0.5951 | 2650 | 0.1091 | - |
441
+ | 0.6063 | 2700 | 0.0031 | - |
442
+ | 0.6176 | 2750 | 0.0181 | - |
443
+ | 0.6288 | 2800 | 0.1366 | - |
444
+ | 0.6400 | 2850 | 0.0178 | - |
445
+ | 0.6512 | 2900 | 0.0571 | - |
446
+ | 0.6625 | 2950 | 0.0271 | - |
447
+ | 0.6737 | 3000 | 0.0368 | - |
448
+ | 0.6849 | 3050 | 0.0652 | - |
449
+ | 0.6962 | 3100 | 0.0858 | - |
450
+ | 0.7074 | 3150 | 0.016 | - |
451
+ | 0.7186 | 3200 | 0.0318 | - |
452
+ | 0.7298 | 3250 | 0.0119 | - |
453
+ | 0.7411 | 3300 | 0.0314 | - |
454
+ | 0.7523 | 3350 | 0.008 | - |
455
+ | 0.7635 | 3400 | 0.0192 | - |
456
+ | 0.7748 | 3450 | 0.0363 | - |
457
+ | 0.7860 | 3500 | 0.0474 | - |
458
+ | 0.7972 | 3550 | 0.0172 | - |
459
+ | 0.8084 | 3600 | 0.0308 | - |
460
+ | 0.8197 | 3650 | 0.1168 | - |
461
+ | 0.8309 | 3700 | 0.0367 | - |
462
+ | 0.8421 | 3750 | 0.1572 | - |
463
+ | 0.8534 | 3800 | 0.0865 | - |
464
+ | 0.8646 | 3850 | 0.0124 | - |
465
+ | 0.8758 | 3900 | 0.0674 | - |
466
+ | 0.8870 | 3950 | 0.0534 | - |
467
+ | 0.8983 | 4000 | 0.0042 | - |
468
+ | 0.9095 | 4050 | 0.0503 | - |
469
+ | 0.9207 | 4100 | 0.0753 | - |
470
+ | 0.9320 | 4150 | 0.0079 | - |
471
+ | 0.9432 | 4200 | 0.1386 | - |
472
+ | 0.9544 | 4250 | 0.0693 | - |
473
+ | 0.9656 | 4300 | 0.0505 | - |
474
+ | 0.9769 | 4350 | 0.0153 | - |
475
+ | 0.9881 | 4400 | 0.0456 | - |
476
+ | 0.9993 | 4450 | 0.077 | - |
477
+ | 1.0 | 4453 | - | 0.1885 |
478
+ | 1.0106 | 4500 | 0.0107 | - |
479
+ | 1.0218 | 4550 | 0.0533 | - |
480
+ | 1.0330 | 4600 | 0.0069 | - |
481
+ | 1.0442 | 4650 | 0.0073 | - |
482
+ | 1.0555 | 4700 | 0.0521 | - |
483
+ | 1.0667 | 4750 | 0.0084 | - |
484
+ | 1.0779 | 4800 | 0.0443 | - |
485
+ | 1.0892 | 4850 | 0.0504 | - |
486
+ | 1.1004 | 4900 | 0.0445 | - |
487
+ | 1.1116 | 4950 | 0.0169 | - |
488
+ | 1.1228 | 5000 | 0.016 | - |
489
+ | 1.1341 | 5050 | 0.0046 | - |
490
+ | 1.1453 | 5100 | 0.0103 | - |
491
+ | 1.1565 | 5150 | 0.0404 | - |
492
+ | 1.1678 | 5200 | 0.0117 | - |
493
+ | 1.1790 | 5250 | 0.0399 | - |
494
+ | 1.1902 | 5300 | 0.0598 | - |
495
+ | 1.2014 | 5350 | 0.015 | - |
496
+ | 1.2127 | 5400 | 0.0048 | - |
497
+ | 1.2239 | 5450 | 0.0047 | - |
498
+ | 1.2351 | 5500 | 0.0042 | - |
499
+ | 1.2464 | 5550 | 0.0106 | - |
500
+ | 1.2576 | 5600 | 0.0041 | - |
501
+ | 1.2688 | 5650 | 0.1593 | - |
502
+ | 1.2800 | 5700 | 0.0386 | - |
503
+ | 1.2913 | 5750 | 0.0059 | - |
504
+ | 1.3025 | 5800 | 0.0043 | - |
505
+ | 1.3137 | 5850 | 0.0039 | - |
506
+ | 1.3249 | 5900 | 0.0101 | - |
507
+ | 1.3362 | 5950 | 0.0043 | - |
508
+ | 1.3474 | 6000 | 0.0056 | - |
509
+ | 1.3586 | 6050 | 0.002 | - |
510
+ | 1.3699 | 6100 | 0.0064 | - |
511
+ | 1.3811 | 6150 | 0.0106 | - |
512
+ | 1.3923 | 6200 | 0.03 | - |
513
+ | 1.4035 | 6250 | 0.0945 | - |
514
+ | 1.4148 | 6300 | 0.0025 | - |
515
+ | 1.4260 | 6350 | 0.0631 | - |
516
+ | 1.4372 | 6400 | 0.0068 | - |
517
+ | 1.4485 | 6450 | 0.0583 | - |
518
+ | 1.4597 | 6500 | 0.0015 | - |
519
+ | 1.4709 | 6550 | 0.0042 | - |
520
+ | 1.4821 | 6600 | 0.0093 | - |
521
+ | 1.4934 | 6650 | 0.0046 | - |
522
+ | 1.5046 | 6700 | 0.009 | - |
523
+ | 1.5158 | 6750 | 0.0279 | - |
524
+ | 1.5271 | 6800 | 0.0357 | - |
525
+ | 1.5383 | 6850 | 0.0282 | - |
526
+ | 1.5495 | 6900 | 0.0188 | - |
527
+ | 1.5607 | 6950 | 0.0405 | - |
528
+ | 1.5720 | 7000 | 0.0645 | - |
529
+ | 1.5832 | 7050 | 0.0066 | - |
530
+ | 1.5944 | 7100 | 0.0205 | - |
531
+ | 1.6057 | 7150 | 0.0038 | - |
532
+ | 1.6169 | 7200 | 0.0696 | - |
533
+ | 1.6281 | 7250 | 0.0055 | - |
534
+ | 1.6393 | 7300 | 0.0034 | - |
535
+ | 1.6506 | 7350 | 0.006 | - |
536
+ | 1.6618 | 7400 | 0.015 | - |
537
+ | 1.6730 | 7450 | 0.0023 | - |
538
+ | 1.6843 | 7500 | 0.0173 | - |
539
+ | 1.6955 | 7550 | 0.0601 | - |
540
+ | 1.7067 | 7600 | 0.0039 | - |
541
+ | 1.7179 | 7650 | 0.0201 | - |
542
+ | 1.7292 | 7700 | 0.0206 | - |
543
+ | 1.7404 | 7750 | 0.0042 | - |
544
+ | 1.7516 | 7800 | 0.0156 | - |
545
+ | 1.7629 | 7850 | 0.002 | - |
546
+ | 1.7741 | 7900 | 0.0059 | - |
547
+ | 1.7853 | 7950 | 0.0327 | - |
548
+ | 1.7965 | 8000 | 0.0206 | - |
549
+ | 1.8078 | 8050 | 0.0698 | - |
550
+ | 1.8190 | 8100 | 0.0217 | - |
551
+ | 1.8302 | 8150 | 0.0309 | - |
552
+ | 1.8415 | 8200 | 0.0136 | - |
553
+ | 1.8527 | 8250 | 0.0455 | - |
554
+ | 1.8639 | 8300 | 0.0645 | - |
555
+ | 1.8751 | 8350 | 0.0127 | - |
556
+ | 1.8864 | 8400 | 0.0056 | - |
557
+ | 1.8976 | 8450 | 0.0127 | - |
558
+ | 1.9088 | 8500 | 0.0024 | - |
559
+ | 1.9201 | 8550 | 0.0117 | - |
560
+ | 1.9313 | 8600 | 0.0626 | - |
561
+ | 1.9425 | 8650 | 0.0357 | - |
562
+ | 1.9537 | 8700 | 0.056 | - |
563
+ | 1.9650 | 8750 | 0.0311 | - |
564
+ | 1.9762 | 8800 | 0.0123 | - |
565
+ | 1.9874 | 8850 | 0.0638 | - |
566
+ | 1.9987 | 8900 | 0.0328 | - |
567
+ | 2.0 | 8906 | - | 0.2196 |
568
+ | 2.0099 | 8950 | 0.0015 | - |
569
+ | 2.0211 | 9000 | 0.0178 | - |
570
+ | 2.0323 | 9050 | 0.08 | - |
571
+ | 2.0436 | 9100 | 0.0983 | - |
572
+ | 2.0548 | 9150 | 0.0049 | - |
573
+ | 2.0660 | 9200 | 0.0092 | - |
574
+ | 2.0773 | 9250 | 0.0619 | - |
575
+ | 2.0885 | 9300 | 0.0159 | - |
576
+ | 2.0997 | 9350 | 0.0598 | - |
577
+ | 2.1109 | 9400 | 0.0343 | - |
578
+ | 2.1222 | 9450 | 0.0092 | - |
579
+ | 2.1334 | 9500 | 0.0013 | - |
580
+ | 2.1446 | 9550 | 0.0042 | - |
581
+ | 2.1558 | 9600 | 0.0059 | - |
582
+ | 2.1671 | 9650 | 0.0076 | - |
583
+ | 2.1783 | 9700 | 0.0027 | - |
584
+ | 2.1895 | 9750 | 0.0174 | - |
585
+ | 2.2008 | 9800 | 0.0044 | - |
586
+ | 2.2120 | 9850 | 0.0164 | - |
587
+ | 2.2232 | 9900 | 0.0015 | - |
588
+ | 2.2344 | 9950 | 0.0026 | - |
589
+ | 2.2457 | 10000 | 0.0118 | - |
590
+ | 2.2569 | 10050 | 0.0054 | - |
591
+ | 2.2681 | 10100 | 0.0016 | - |
592
+ | 2.2794 | 10150 | 0.0095 | - |
593
+ | 2.2906 | 10200 | 0.0157 | - |
594
+ | 2.3018 | 10250 | 0.0465 | - |
595
+ | 2.3130 | 10300 | 0.0024 | - |
596
+ | 2.3243 | 10350 | 0.0009 | - |
597
+ | 2.3355 | 10400 | 0.0101 | - |
598
+ | 2.3467 | 10450 | 0.0266 | - |
599
+ | 2.3580 | 10500 | 0.0022 | - |
600
+ | 2.3692 | 10550 | 0.0016 | - |
601
+ | 2.3804 | 10600 | 0.0096 | - |
602
+ | 2.3916 | 10650 | 0.0052 | - |
603
+ | 2.4029 | 10700 | 0.0656 | - |
604
+ | 2.4141 | 10750 | 0.0481 | - |
605
+ | 2.4253 | 10800 | 0.0148 | - |
606
+ | 2.4366 | 10850 | 0.0024 | - |
607
+ | 2.4478 | 10900 | 0.0039 | - |
608
+ | 2.4590 | 10950 | 0.0011 | - |
609
+ | 2.4702 | 11000 | 0.0142 | - |
610
+ | 2.4815 | 11050 | 0.0617 | - |
611
+ | 2.4927 | 11100 | 0.0069 | - |
612
+ | 2.5039 | 11150 | 0.0063 | - |
613
+ | 2.5152 | 11200 | 0.0218 | - |
614
+ | 2.5264 | 11250 | 0.0018 | - |
615
+ | 2.5376 | 11300 | 0.0017 | - |
616
+ | 2.5488 | 11350 | 0.0105 | - |
617
+ | 2.5601 | 11400 | 0.0019 | - |
618
+ | 2.5713 | 11450 | 0.0027 | - |
619
+ | 2.5825 | 11500 | 0.0616 | - |
620
+ | 2.5938 | 11550 | 0.0704 | - |
621
+ | 2.6050 | 11600 | 0.0047 | - |
622
+ | 2.6162 | 11650 | 0.0106 | - |
623
+ | 2.6274 | 11700 | 0.0067 | - |
624
+ | 2.6387 | 11750 | 0.0272 | - |
625
+ | 2.6499 | 11800 | 0.0476 | - |
626
+ | 2.6611 | 11850 | 0.0401 | - |
627
+ | 2.6724 | 11900 | 0.0017 | - |
628
+ | 2.6836 | 11950 | 0.0247 | - |
629
+ | 2.6948 | 12000 | 0.0173 | - |
630
+ | 2.7060 | 12050 | 0.0129 | - |
631
+ | 2.7173 | 12100 | 0.0041 | - |
632
+ | 2.7285 | 12150 | 0.0017 | - |
633
+ | 2.7397 | 12200 | 0.0137 | - |
634
+ | 2.7510 | 12250 | 0.0629 | - |
635
+ | 2.7622 | 12300 | 0.034 | - |
636
+ | 2.7734 | 12350 | 0.0533 | - |
637
+ | 2.7846 | 12400 | 0.057 | - |
638
+ | 2.7959 | 12450 | 0.0153 | - |
639
+ | 2.8071 | 12500 | 0.0023 | - |
640
+ | 2.8183 | 12550 | 0.0013 | - |
641
+ | 2.8296 | 12600 | 0.0014 | - |
642
+ | 2.8408 | 12650 | 0.0023 | - |
643
+ | 2.8520 | 12700 | 0.0026 | - |
644
+ | 2.8632 | 12750 | 0.0027 | - |
645
+ | 2.8745 | 12800 | 0.0064 | - |
646
+ | 2.8857 | 12850 | 0.0174 | - |
647
+ | 2.8969 | 12900 | 0.0017 | - |
648
+ | 2.9082 | 12950 | 0.0242 | - |
649
+ | 2.9194 | 13000 | 0.0487 | - |
650
+ | 2.9306 | 13050 | 0.0022 | - |
651
+ | 2.9418 | 13100 | 0.0108 | - |
652
+ | 2.9531 | 13150 | 0.0079 | - |
653
+ | 2.9643 | 13200 | 0.0108 | - |
654
+ | 2.9755 | 13250 | 0.0027 | - |
655
+ | 2.9868 | 13300 | 0.0053 | - |
656
+ | 2.9980 | 13350 | 0.0039 | - |
657
+ | 3.0 | 13359 | - | 0.2038 |
658
+ | 3.0092 | 13400 | 0.0089 | - |
659
+ | 3.0204 | 13450 | 0.0369 | - |
660
+ | 3.0317 | 13500 | 0.0107 | - |
661
+ | 3.0429 | 13550 | 0.0187 | - |
662
+ | 3.0541 | 13600 | 0.0038 | - |
663
+ | 3.0653 | 13650 | 0.0072 | - |
664
+ | 3.0766 | 13700 | 0.005 | - |
665
+ | 3.0878 | 13750 | 0.0192 | - |
666
+ | 3.0990 | 13800 | 0.0084 | - |
667
+ | 3.1103 | 13850 | 0.002 | - |
668
+ | 3.1215 | 13900 | 0.0011 | - |
669
+ | 3.1327 | 13950 | 0.0037 | - |
670
+ | 3.1439 | 14000 | 0.0087 | - |
671
+ | 3.1552 | 14050 | 0.0014 | - |
672
+ | 3.1664 | 14100 | 0.0029 | - |
673
+ | 3.1776 | 14150 | 0.0176 | - |
674
+ | 3.1889 | 14200 | 0.0028 | - |
675
+ | 3.2001 | 14250 | 0.012 | - |
676
+ | 3.2113 | 14300 | 0.0933 | - |
677
+ | 3.2225 | 14350 | 0.002 | - |
678
+ | 3.2338 | 14400 | 0.053 | - |
679
+ | 3.2450 | 14450 | 0.0117 | - |
680
+ | 3.2562 | 14500 | 0.0227 | - |
681
+ | 3.2675 | 14550 | 0.0055 | - |
682
+ | 3.2787 | 14600 | 0.008 | - |
683
+ | 3.2899 | 14650 | 0.0512 | - |
684
+ | 3.3011 | 14700 | 0.0025 | - |
685
+ | 3.3124 | 14750 | 0.0432 | - |
686
+ | 3.3236 | 14800 | 0.002 | - |
687
+ | 3.3348 | 14850 | 0.013 | - |
688
+ | 3.3461 | 14900 | 0.0026 | - |
689
+ | 3.3573 | 14950 | 0.0022 | - |
690
+ | 3.3685 | 15000 | 0.0225 | - |
691
+ | 3.3797 | 15050 | 0.0611 | - |
692
+ | 3.3910 | 15100 | 0.0261 | - |
693
+ | 3.4022 | 15150 | 0.0026 | - |
694
+ | 3.4134 | 15200 | 0.004 | - |
695
+ | 3.4247 | 15250 | 0.0054 | - |
696
+ | 3.4359 | 15300 | 0.0132 | - |
697
+ | 3.4471 | 15350 | 0.0017 | - |
698
+ | 3.4583 | 15400 | 0.0213 | - |
699
+ | 3.4696 | 15450 | 0.007 | - |
700
+ | 3.4808 | 15500 | 0.0507 | - |
701
+ | 3.4920 | 15550 | 0.0039 | - |
702
+ | 3.5033 | 15600 | 0.0059 | - |
703
+ | 3.5145 | 15650 | 0.0357 | - |
704
+ | 3.5257 | 15700 | 0.0009 | - |
705
+ | 3.5369 | 15750 | 0.0014 | - |
706
+ | 3.5482 | 15800 | 0.0011 | - |
707
+ | 3.5594 | 15850 | 0.0082 | - |
708
+ | 3.5706 | 15900 | 0.001 | - |
709
+ | 3.5819 | 15950 | 0.0045 | - |
710
+ | 3.5931 | 16000 | 0.0205 | - |
711
+ | 3.6043 | 16050 | 0.0096 | - |
712
+ | 3.6155 | 16100 | 0.0286 | - |
713
+ | 3.6268 | 16150 | 0.0043 | - |
714
+ | 3.6380 | 16200 | 0.0029 | - |
715
+ | 3.6492 | 16250 | 0.0079 | - |
716
+ | 3.6605 | 16300 | 0.0036 | - |
717
+ | 3.6717 | 16350 | 0.0013 | - |
718
+ | 3.6829 | 16400 | 0.0086 | - |
719
+ | 3.6941 | 16450 | 0.0049 | - |
720
+ | 3.7054 | 16500 | 0.0006 | - |
721
+ | 3.7166 | 16550 | 0.0467 | - |
722
+ | 3.7278 | 16600 | 0.002 | - |
723
+ | 3.7391 | 16650 | 0.0229 | - |
724
+ | 3.7503 | 16700 | 0.0532 | - |
725
+ | 3.7615 | 16750 | 0.001 | - |
726
+ | 3.7727 | 16800 | 0.0034 | - |
727
+ | 3.7840 | 16850 | 0.0117 | - |
728
+ | 3.7952 | 16900 | 0.0424 | - |
729
+ | 3.8064 | 16950 | 0.0032 | - |
730
+ | 3.8177 | 17000 | 0.0024 | - |
731
+ | 3.8289 | 17050 | 0.0011 | - |
732
+ | 3.8401 | 17100 | 0.0024 | - |
733
+ | 3.8513 | 17150 | 0.0059 | - |
734
+ | 3.8626 | 17200 | 0.0005 | - |
735
+ | 3.8738 | 17250 | 0.0074 | - |
736
+ | 3.8850 | 17300 | 0.0517 | - |
737
+ | 3.8962 | 17350 | 0.0081 | - |
738
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739
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740
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741
+ | 3.9412 | 17550 | 0.0008 | - |
742
+ | 3.9524 | 17600 | 0.0094 | - |
743
+ | 3.9636 | 17650 | 0.001 | - |
744
+ | 3.9748 | 17700 | 0.0069 | - |
745
+ | 3.9861 | 17750 | 0.002 | - |
746
+ | 3.9973 | 17800 | 0.003 | - |
747
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748
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749
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750
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796
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812
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814
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815
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818
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823
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824
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825
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826
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827
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828
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829
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830
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831
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832
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833
+ | 4.9629 | 22100 | 0.0437 | - |
834
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835
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836
+ | 4.9966 | 22250 | 0.0015 | - |
837
+ | 5.0 | 22265 | - | 0.2357 |
838
+
839
+ ### Framework Versions
840
+ - Python: 3.10.12
841
+ - SetFit: 1.0.3
842
+ - Sentence Transformers: 2.2.2
843
+ - Transformers: 4.36.1
844
+ - PyTorch: 2.0.1+cu118
845
+ - Datasets: 2.15.0
846
+ - Tokenizers: 0.15.0
847
+
848
+ ## Citation
849
+
850
+ ### BibTeX
851
+ ```bibtex
852
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
853
+ doi = {10.48550/ARXIV.2209.11055},
854
+ url = {https://arxiv.org/abs/2209.11055},
855
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
856
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
857
+ title = {Efficient Few-Shot Learning Without Prompts},
858
+ publisher = {arXiv},
859
+ year = {2022},
860
+ copyright = {Creative Commons Attribution 4.0 International}
861
+ }
862
+ ```
863
+
864
+ <!--
865
+ ## Glossary
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+
867
+ *Clearly define terms in order to be accessible across audiences.*
868
+ -->
869
+
870
+ <!--
871
+ ## Model Card Authors
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+
873
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
874
+ -->
875
+
876
+ <!--
877
+ ## Model Card Contact
878
+
879
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
880
+ -->
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