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Aurore32
/
physics-classifier-model

Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Eval Results
Model card Files Community
Aurore32 commited on
Commit
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Browse files
Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +790 -0
  3. config.json +24 -0
  4. config_sentence_transformers.json +10 -0
  5. config_setfit.json +590 -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
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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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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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+ }
README.md ADDED
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+ ---
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+ base_model: sentence-transformers/paraphrase-mpnet-base-v2
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+ library_name: setfit
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+ metrics:
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+ - accuracy
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+ pipeline_tag: text-classification
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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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+ widget:
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+ - text: handling washing radiation precaution long leaving scientist tongs source
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+ effective nature air range row laboratory low experiment keeping sealed temperature
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+ opening protect
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+ - text: nucleus proton element charge isotope
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+ - text: size mean al time sound tuning frequency every marked last second length note
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+ directly produced fork number travel
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+ - text: distance circle surface stick frequency water dipped correct vertical longitudinal
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+ three produced statement two amplitude wave
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+ - text: rate electromagnetic radiation source correct form stream radioactive penetrating
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+ detector statement highly
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+ inference: true
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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: 0.9387755102040817
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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:** 6 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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+ | wave | <ul><li>'sound infrared wave ultraviolet transverse list diagram light'</li><li>'size mean al time sound tuning frequency every marked last second length note directly produced fork number travel'</li><li>'nearby series sound higher frequency air rarefaction correctly pressure amplitude compression row certain lower wave'</li></ul> |
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+ | forces | <ul><li>'extension diagram spring free load three applied handle'</li><li>'different extension spring elastic student proportional length directly object weight diagram cord mass much hung'</li><li>'distance plank long ground load force beam mass value'</li></ul> |
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+ | electricity | <ul><li>'two diagram circuit parallel connected'</li><li>'lamp resistance student various thermistor current circuit parallel happen'</li><li>'resistance student two shown reading ammeter resistor identical circuit voltmeter determine'</li></ul> |
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+ | magnetism | <ul><li>'bar suitable two across made magnet metal electromagnet diagram permanent core'</li><li>'magnetic student hard magnet rod iron material magnetism correct use statement steel permanent soft make'</li><li>'magnetic bar student two hard magnet rod iron material close nickel use steel permanent soft make'</li></ul> |
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+ | nuclear | <ul><li>'nucleus proton element charge isotope'</li><li>'different five'</li><li>'rate electromagnetic radiation source correct form stream radioactive penetrating detector statement highly'</li></ul> |
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+ | thermal | <ul><li>'cooling air top unit stay density transfer correct near thermal move diagram statement refrigerator energy movement'</li><li>'dense position air cold warmer heater flask two room fit less hot possible fitted diagram warm throughout um liquid better'</li><li>'power balance copper student thermometer heat heater substance watch calculate need specific initially solid mass apparatus block capacity electrical key'</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** | 0.9388 |
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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("Aurore32/physics-classifier-model")
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+ # Run inference
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+ preds = model("nucleus proton element charge isotope")
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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 | 2 | 13.375 | 35 |
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+
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+ | Label | Training Sample Count |
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+ |:------------|:----------------------|
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+ | wave | 8 |
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+ | wave | 8 |
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+ | magnetism | 8 |
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+ | wave | 8 |
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+ | magnetism | 8 |
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+ | thermal | 8 |
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+ | thermal | 8 |
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+ | thermal | 8 |
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+ | nuclear | 8 |
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+ | electricity | 8 |
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+ | electricity | 8 |
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+ | thermal | 8 |
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+ | forces | 8 |
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+ | electricity | 8 |
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+ | nuclear | 8 |
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+ | thermal | 8 |
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+ | magnetism | 8 |
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+ | forces | 8 |
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+ | wave | 8 |
553
+ | thermal | 8 |
554
+ | wave | 8 |
555
+ | forces | 8 |
556
+ | thermal | 8 |
557
+ | magnetism | 8 |
558
+ | electricity | 8 |
559
+ | magnetism | 8 |
560
+ | wave | 8 |
561
+ | nuclear | 8 |
562
+ | wave | 8 |
563
+ | forces | 8 |
564
+ | thermal | 8 |
565
+ | magnetism | 8 |
566
+ | forces | 8 |
567
+ | thermal | 8 |
568
+ | wave | 8 |
569
+ | magnetism | 8 |
570
+ | thermal | 8 |
571
+ | thermal | 8 |
572
+ | electricity | 8 |
573
+ | nuclear | 8 |
574
+ | wave | 8 |
575
+ | nuclear | 8 |
576
+ | magnetism | 8 |
577
+ | magnetism | 8 |
578
+ | thermal | 8 |
579
+ | magnetism | 8 |
580
+ | electricity | 8 |
581
+ | magnetism | 8 |
582
+ | magnetism | 8 |
583
+ | thermal | 8 |
584
+ | wave | 8 |
585
+ | nuclear | 8 |
586
+ | nuclear | 8 |
587
+ | thermal | 8 |
588
+ | wave | 8 |
589
+ | magnetism | 8 |
590
+ | nuclear | 8 |
591
+ | nuclear | 8 |
592
+ | thermal | 8 |
593
+ | forces | 8 |
594
+ | wave | 8 |
595
+ | forces | 8 |
596
+ | wave | 8 |
597
+ | nuclear | 8 |
598
+ | forces | 8 |
599
+ | wave | 8 |
600
+ | magnetism | 8 |
601
+ | nuclear | 8 |
602
+ | wave | 8 |
603
+ | magnetism | 8 |
604
+ | electricity | 8 |
605
+ | electricity | 8 |
606
+ | wave | 8 |
607
+ | nuclear | 8 |
608
+ | electricity | 8 |
609
+ | electricity | 8 |
610
+ | wave | 8 |
611
+ | wave | 8 |
612
+ | forces | 8 |
613
+ | nuclear | 8 |
614
+ | nuclear | 8 |
615
+ | magnetism | 8 |
616
+ | thermal | 8 |
617
+ | nuclear | 8 |
618
+ | nuclear | 8 |
619
+ | thermal | 8 |
620
+ | wave | 8 |
621
+ | nuclear | 8 |
622
+ | forces | 8 |
623
+ | nuclear | 8 |
624
+ | thermal | 8 |
625
+ | magnetism | 8 |
626
+ | wave | 8 |
627
+ | nuclear | 8 |
628
+ | thermal | 8 |
629
+ | thermal | 8 |
630
+ | magnetism | 8 |
631
+ | electricity | 8 |
632
+ | wave | 8 |
633
+ | wave | 8 |
634
+ | electricity | 8 |
635
+ | wave | 8 |
636
+ | thermal | 8 |
637
+ | thermal | 8 |
638
+ | thermal | 8 |
639
+ | nuclear | 8 |
640
+ | forces | 8 |
641
+ | wave | 8 |
642
+ | electricity | 8 |
643
+ | wave | 8 |
644
+ | forces | 8 |
645
+ | thermal | 8 |
646
+ | thermal | 8 |
647
+ | magnetism | 8 |
648
+ | wave | 8 |
649
+ | electricity | 8 |
650
+ | nuclear | 8 |
651
+ | thermal | 8 |
652
+ | thermal | 8 |
653
+ | thermal | 8 |
654
+ | thermal | 8 |
655
+ | thermal | 8 |
656
+ | electricity | 8 |
657
+ | nuclear | 8 |
658
+ | thermal | 8 |
659
+ | nuclear | 8 |
660
+ | wave | 8 |
661
+ | wave | 8 |
662
+ | forces | 8 |
663
+ | electricity | 8 |
664
+ | thermal | 8 |
665
+ | wave | 8 |
666
+ | nuclear | 8 |
667
+ | nuclear | 8 |
668
+ | thermal | 8 |
669
+ | wave | 8 |
670
+ | magnetism | 8 |
671
+ | nuclear | 8 |
672
+ | wave | 8 |
673
+ | nuclear | 8 |
674
+ | wave | 8 |
675
+ | wave | 8 |
676
+ | magnetism | 8 |
677
+ | nuclear | 8 |
678
+ | forces | 8 |
679
+ | magnetism | 8 |
680
+ | magnetism | 8 |
681
+ | wave | 8 |
682
+ | nuclear | 8 |
683
+ | forces | 8 |
684
+ | magnetism | 8 |
685
+ | thermal | 8 |
686
+ | wave | 8 |
687
+ | magnetism | 8 |
688
+ | thermal | 8 |
689
+ | nuclear | 8 |
690
+ | wave | 8 |
691
+ | electricity | 8 |
692
+ | wave | 8 |
693
+ | magnetism | 8 |
694
+ | electricity | 8 |
695
+ | magnetism | 8 |
696
+ | thermal | 8 |
697
+ | forces | 8 |
698
+ | forces | 8 |
699
+ | thermal | 8 |
700
+ | wave | 8 |
701
+ | electricity | 8 |
702
+ | wave | 8 |
703
+ | electricity | 8 |
704
+ | wave | 8 |
705
+ | thermal | 8 |
706
+ | thermal | 8 |
707
+ | nuclear | 8 |
708
+ | electricity | 8 |
709
+ | forces | 8 |
710
+ | nuclear | 8 |
711
+ | magnetism | 8 |
712
+ | magnetism | 8 |
713
+ | wave | 8 |
714
+ | nuclear | 8 |
715
+ | forces | 8 |
716
+ | wave | 8 |
717
+ | forces | 8 |
718
+ | magnetism | 8 |
719
+ | thermal | 8 |
720
+ | nuclear | 8 |
721
+ | thermal | 8 |
722
+ | electricity | 8 |
723
+
724
+ ### Training Hyperparameters
725
+ - batch_size: (16, 2)
726
+ - num_epochs: (1, 16)
727
+ - max_steps: -1
728
+ - sampling_strategy: oversampling
729
+ - body_learning_rate: (2e-05, 1e-05)
730
+ - head_learning_rate: 0.01
731
+ - loss: CosineSimilarityLoss
732
+ - distance_metric: cosine_distance
733
+ - margin: 0.25
734
+ - end_to_end: False
735
+ - use_amp: False
736
+ - warmup_proportion: 0.1
737
+ - l2_weight: 0.01
738
+ - seed: 42
739
+ - eval_max_steps: -1
740
+ - load_best_model_at_end: False
741
+
742
+ ### Training Results
743
+ | Epoch | Step | Training Loss | Validation Loss |
744
+ |:------:|:----:|:-------------:|:---------------:|
745
+ | 0.0083 | 1 | 0.1733 | - |
746
+ | 0.4167 | 50 | 0.075 | - |
747
+ | 0.8333 | 100 | 0.0056 | - |
748
+
749
+ ### Framework Versions
750
+ - Python: 3.12.4
751
+ - SetFit: 1.1.1
752
+ - Sentence Transformers: 3.4.0
753
+ - Transformers: 4.44.2
754
+ - PyTorch: 2.5.1+cpu
755
+ - Datasets: 3.2.0
756
+ - Tokenizers: 0.19.1
757
+
758
+ ## Citation
759
+
760
+ ### BibTeX
761
+ ```bibtex
762
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
763
+ doi = {10.48550/ARXIV.2209.11055},
764
+ url = {https://arxiv.org/abs/2209.11055},
765
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
766
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
767
+ title = {Efficient Few-Shot Learning Without Prompts},
768
+ publisher = {arXiv},
769
+ year = {2022},
770
+ copyright = {Creative Commons Attribution 4.0 International}
771
+ }
772
+ ```
773
+
774
+ <!--
775
+ ## Glossary
776
+
777
+ *Clearly define terms in order to be accessible across audiences.*
778
+ -->
779
+
780
+ <!--
781
+ ## Model Card Authors
782
+
783
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
784
+ -->
785
+
786
+ <!--
787
+ ## Model Card Contact
788
+
789
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
790
+ -->
config.json ADDED
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+ {
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+ "_name_or_path": "sentence-transformers/paraphrase-mpnet-base-v2",
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+ "architectures": [
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+ "MPNetModel"
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+ ],
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+ "attention_probs_dropout_prob": 0.1,
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+ "bos_token_id": 0,
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+ "eos_token_id": 2,
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.1,
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+ "hidden_size": 768,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 3072,
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+ "layer_norm_eps": 1e-05,
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+ "max_position_embeddings": 514,
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+ "model_type": "mpnet",
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+ "num_attention_heads": 12,
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+ "num_hidden_layers": 12,
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+ "pad_token_id": 1,
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+ "relative_attention_num_buckets": 32,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.44.2",
23
+ "vocab_size": 30527
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+ }
config_sentence_transformers.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "__version__": {
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+ "sentence_transformers": "3.4.0",
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+ "transformers": "4.44.2",
5
+ "pytorch": "2.5.1+cpu"
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+ },
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+ "prompts": {},
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+ "default_prompt_name": null,
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+ "similarity_fn_name": "cosine"
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+ }
config_setfit.json ADDED
@@ -0,0 +1,590 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "normalize_embeddings": false,
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+ "labels": [
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+ "wave",
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+ "wave",
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+ "magnetism",
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+ "wave",
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