--- library_name: setfit tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer metrics: - accuracy widget: - text: Despite the overall trend of mass loss, some glaciers in the western Himalayas exhibited small gains in mass between 2000 and 2016. - text: This study focuses on analyzing traffic data from roads with a design speed of at least 80 km/h and a daily traffic volume exceeding 10,000 vehicles. - text: This paper explores the intricacies of memory storage at the molecular level, focusing on the interplay between genes and synapses. - text: This chapter explores the calculation of chemical reactivity indexes using density functional theory, providing a comprehensive analysis of their applicability and accuracy in predicting chemical reactions. - text: Prospect theory, introduced by Kahneman and Tversky (1979), has significantly advanced our understanding of decision-making under risk and uncertainty in mathematical economics and behavioral finance. pipeline_tag: text-classification inference: true base_model: sentence-transformers/all-MiniLM-L6-v2 model-index: - name: SetFit with sentence-transformers/all-MiniLM-L6-v2 results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: accuracy value: 0.7116504854368932 name: Accuracy --- # SetFit with sentence-transformers/all-MiniLM-L6-v2 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - **Model Type:** SetFit - **Sentence Transformer body:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - **Maximum Sequence Length:** 256 tokens - **Number of Classes:** 103 classes ### Model Sources - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels | Label | Examples | |:-------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Acknowledging limitation(s) whilst stating a finding or contribution | | | Advising cautious interpretation of the findings | | | Commenting on the findings | | | Commenting on the strengths of the current study | | | Comparing the result: contradicting previous findings | | | Comparing the result: supporting previous findings | | | Contrasting sources with ‘however’ for emphasis | | | Describing previously used methods | | | Describing questionnaire design | | | Describing the characteristics of the participants | | | Describing the limitations of the current study | | | Describing the process: adverbs of manner | | | Describing the process: expressing purpose with for | | | Describing the process: infinitive of purpose | | | Describing the process: sequence words | | | Describing the process: statistical procedures | | | Describing the process: typical verbs in the passive form | | | Describing the process: using + instrument | | | Describing the research design and the methods used | | | Describing what other writers do in their published work | | | Detailing specific limitations | | | Establishing the importance of the topic for the discipline | | | Establishing the importance of the topic for the discipline: time frame given | | | Establishing the importance of the topic for the world or society | | | Establishing the importance of the topic for the world or society: time frame given | | | Establising the importance of the topic as a problem to be addressed | | | Explaining keywords (also refer to Defining Terms) | | | Explaining the provenance of articles for review | | | Explaining the provenance of the participants | | | Explaining the significance of the current study | | | Explaining the significance of the findings or contribution of the study | | | General comments on the relevant literature | | | General reference to previous research or scholarship: highlighting negative outcomes | | | Giving reasons for personal interest in the research (sometimes found in the humanities, and the applied human sciences) | | | Giving reasons why a particular method was adopted | | | Giving reasons why a particular method was rejected | | | Highlighting inadequacies or weaknesses of previous studies (also refer to Being Critical) | | | Highlighting interesting or surprising results | | | Highlighting significant data in a table or chart | | | Identifying a controversy within the field of study | | | Identifying a knowledge gap in the field of study | | | Implications and/or recommendations for practice or policy | | | Indicating an expected outcome | | | Indicating an unexpected outcome | | | Indicating criteria for selection or inclusion in the study | | | Indicating methodological problems or limitations | | | Indicating missing, weak, or contradictory evidence | | | Indicating the methodology for the current research | | | Indicating the use of an established method | | | Introducing the limitations of the current study | | | Making recommendations for further research work | | | Noting implications of the findings | | | Noting the lack of or paucity of previous research | | | Offering an explanation for the findings | | | Outlining the structure of a short paper | | | Outlining the structure of a thesis or dissertation | | | Pointing out interesting or important findings | | | Previewing a chapter | | | Previous research: A historic perspective | | | Previous research: Approaches taken | | | Previous research: What has been established or proposed | | | Previous research: area investigated as the sentence object | | | Previous research: area investigated as the sentence subject | | | Previous research: highlighting negative outcomes | | | Providing background information: reference to the literature | | | Providing background information: reference to the purpose of the study | | | Reference to previous research: important studies | | | Referring back to the purpose of the paper or study | | | Referring back to the research aims or procedures | | | Referring to a single investigation in the past: investigation prominent | | | Referring to a single investigation in the past: researcher prominent | | | Referring to another writer’s idea(s) or position | | | Referring to data in a table or chart | | | Referring to important texts in the area of interest | | | Referring to previous work to establish what is already known | | | Referring to secondary sources | | | Referring to the literature to justify a method or approach | | | Reporting positive and negative reactions | | | Restating a result or one of several results | | | Setting out the research questions or hypotheses | | | Some ways of introducing quotations | | | Stating a negative result | | | Stating a positive result | | | Stating purpose of the current research with reference to gaps or issues in the literature | | | Stating the aims of the current research (note frequent use of past tense) | | | Stating the focus, aim, or argument of a short paper | | | Stating the purpose of the thesis, dissertation, or research article (note use of present tense) | | | Stating what is currently known about the topic | | | Suggesting general hypotheses | | | Suggesting implications for what is already known | | | Suggestions for future work | | | Summarising the literature review | | | Summarising the main research findings | | | Summarising the results section | | | Summarising the studies reviewed | | | Surveys and interviews: Introducing excerpts from interview data | | | Surveys and interviews: Reporting participants’ views | | | Surveys and interviews: Reporting proportions | | | Surveys and interviews: Reporting response rates | | | Surveys and interviews: Reporting themes | | | Synthesising sources: contrasting evidence or ideas | | | Synthesising sources: supporting evidence or ideas | | | Transition: moving to the next result | | ## Evaluation ### Metrics | Label | Accuracy | |:--------|:---------| | **all** | 0.7117 | ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("Corran/SciGenSetfit") # Run inference preds = model("Despite the overall trend of mass loss, some glaciers in the western Himalayas exhibited small gains in mass between 2000 and 2016.") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:--------|:----| | Word count | 6 | 28.3746 | 62 | | Label | Training Sample Count | |:-------------------------------------------------------------------------------------------------------------------------|:----------------------| | Acknowledging limitation(s) whilst stating a finding or contribution | 100 | | Advising cautious interpretation of the findings | 100 | | Commenting on the findings | 100 | | Commenting on the strengths of the current study | 100 | | Comparing the result: contradicting previous findings | 100 | | Comparing the result: supporting previous findings | 100 | | Contrasting sources with ‘however’ for emphasis | 100 | | Describing previously used methods | 100 | | Describing questionnaire design | 100 | | Describing the characteristics of the participants | 100 | | Describing the limitations of the current study | 100 | | Describing the process: adverbs of manner | 100 | | Describing the process: expressing purpose with for | 100 | | Describing the process: infinitive of purpose | 100 | | Describing the process: sequence words | 100 | | Describing the process: statistical procedures | 100 | | Describing the process: typical verbs in the passive form | 100 | | Describing the process: using + instrument | 100 | | Describing the research design and the methods used | 100 | | Describing what other writers do in their published work | 100 | | Detailing specific limitations | 100 | | Establishing the importance of the topic for the discipline | 100 | | Establishing the importance of the topic for the discipline: time frame given | 100 | | Establishing the importance of the topic for the world or society | 100 | | Establishing the importance of the topic for the world or society: time frame given | 100 | | Establising the importance of the topic as a problem to be addressed | 100 | | Explaining keywords (also refer to Defining Terms) | 100 | | Explaining the provenance of articles for review | 100 | | Explaining the provenance of the participants | 100 | | Explaining the significance of the current study | 100 | | Explaining the significance of the findings or contribution of the study | 100 | | General comments on the relevant literature | 100 | | General reference to previous research or scholarship: highlighting negative outcomes | 100 | | Giving reasons for personal interest in the research (sometimes found in the humanities, and the applied human sciences) | 100 | | Giving reasons why a particular method was adopted | 100 | | Giving reasons why a particular method was rejected | 100 | | Highlighting inadequacies or weaknesses of previous studies (also refer to Being Critical) | 100 | | Highlighting interesting or surprising results | 100 | | Highlighting significant data in a table or chart | 100 | | Identifying a controversy within the field of study | 100 | | Identifying a knowledge gap in the field of study | 100 | | Implications and/or recommendations for practice or policy | 100 | | Indicating an expected outcome | 100 | | Indicating an unexpected outcome | 100 | | Indicating criteria for selection or inclusion in the study | 100 | | Indicating methodological problems or limitations | 100 | | Indicating missing, weak, or contradictory evidence | 100 | | Indicating the methodology for the current research | 100 | | Indicating the use of an established method | 100 | | Introducing the limitations of the current study | 100 | | Making recommendations for further research work | 100 | | Noting implications of the findings | 100 | | Noting the lack of or paucity of previous research | 100 | | Offering an explanation for the findings | 100 | | Outlining the structure of a short paper | 100 | | Outlining the structure of a thesis or dissertation | 100 | | Pointing out interesting or important findings | 100 | | Previewing a chapter | 100 | | Previous research: A historic perspective | 100 | | Previous research: Approaches taken | 100 | | Previous research: What has been established or proposed | 100 | | Previous research: area investigated as the sentence object | 100 | | Previous research: area investigated as the sentence subject | 100 | | Previous research: highlighting negative outcomes | 100 | | Providing background information: reference to the literature | 100 | | Providing background information: reference to the purpose of the study | 100 | | Reference to previous research: important studies | 100 | | Referring back to the purpose of the paper or study | 100 | | Referring back to the research aims or procedures | 100 | | Referring to a single investigation in the past: investigation prominent | 100 | | Referring to a single investigation in the past: researcher prominent | 100 | | Referring to another writer’s idea(s) or position | 100 | | Referring to data in a table or chart | 100 | | Referring to important texts in the area of interest | 100 | | Referring to previous work to establish what is already known | 100 | | Referring to secondary sources | 100 | | Referring to the literature to justify a method or approach | 100 | | Reporting positive and negative reactions | 100 | | Restating a result or one of several results | 100 | | Setting out the research questions or hypotheses | 100 | | Some ways of introducing quotations | 100 | | Stating a negative result | 100 | | Stating a positive result | 100 | | Stating purpose of the current research with reference to gaps or issues in the literature | 100 | | Stating the aims of the current research (note frequent use of past tense) | 100 | | Stating the focus, aim, or argument of a short paper | 100 | | Stating the purpose of the thesis, dissertation, or research article (note use of present tense) | 100 | | Stating what is currently known about the topic | 100 | | Suggesting general hypotheses | 100 | | Suggesting implications for what is already known | 100 | | Suggestions for future work | 100 | | Summarising the literature review | 100 | | Summarising the main research findings | 100 | | Summarising the results section | 100 | | Summarising the studies reviewed | 100 | | Surveys and interviews: Introducing excerpts from interview data | 100 | | Surveys and interviews: Reporting participants’ views | 100 | | Surveys and interviews: Reporting proportions | 100 | | Surveys and interviews: Reporting response rates | 100 | | Surveys and interviews: Reporting themes | 100 | | Synthesising sources: contrasting evidence or ideas | 100 | | Synthesising sources: supporting evidence or ideas | 100 | | Transition: moving to the next result | 100 | ### Training Hyperparameters - batch_size: (64, 64) - num_epochs: (1, 1) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 80 - body_learning_rate: (2e-05, 1e-05) - head_learning_rate: 0.01 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:------:|:-----:|:-------------:|:---------------:| | 0.0000 | 1 | 0.4689 | - | | 0.0019 | 50 | 0.4588 | - | | 0.0039 | 100 | 0.3724 | - | | 0.0058 | 150 | 0.3633 | - | | 0.0078 | 200 | 0.2659 | - | | 0.0097 | 250 | 0.2067 | - | | 0.0117 | 300 | 0.2045 | - | | 0.0136 | 350 | 0.2199 | - | | 0.0155 | 400 | 0.1913 | - | | 0.0175 | 450 | 0.1658 | - | | 0.0194 | 500 | 0.2027 | - | | 0.0214 | 550 | 0.1458 | - | | 0.0233 | 600 | 0.16 | - | | 0.0252 | 650 | 0.1645 | - | | 0.0272 | 700 | 0.1411 | - | | 0.0291 | 750 | 0.1474 | - | | 0.0311 | 800 | 0.1301 | - | | 0.0330 | 850 | 0.128 | - | | 0.0350 | 900 | 0.1542 | - | | 0.0369 | 950 | 0.0938 | - | | 0.0388 | 1000 | 0.1306 | - | | 0.0408 | 1050 | 0.1405 | - | | 0.0427 | 1100 | 0.1525 | - | | 0.0447 | 1150 | 0.1516 | - | | 0.0466 | 1200 | 0.1016 | - | | 0.0485 | 1250 | 0.1154 | - | | 0.0505 | 1300 | 0.1308 | - | | 0.0524 | 1350 | 0.0993 | - | | 0.0544 | 1400 | 0.1055 | - | | 0.0563 | 1450 | 0.1554 | - | | 0.0583 | 1500 | 0.1418 | - | | 0.0602 | 1550 | 0.0997 | - | | 0.0621 | 1600 | 0.1023 | - | | 0.0641 | 1650 | 0.1383 | - | | 0.0660 | 1700 | 0.0902 | - | | 0.0680 | 1750 | 0.103 | - | | 0.0699 | 1800 | 0.1281 | - | | 0.0718 | 1850 | 0.1273 | - | | 0.0738 | 1900 | 0.1669 | - | | 0.0757 | 1950 | 0.0752 | - | | 0.0777 | 2000 | 0.0795 | - | | 0.0796 | 2050 | 0.129 | - | | 0.0816 | 2100 | 0.0763 | - | | 0.0835 | 2150 | 0.0828 | - | | 0.0854 | 2200 | 0.0901 | - | | 0.0874 | 2250 | 0.0707 | - | | 0.0893 | 2300 | 0.114 | - | | 0.0913 | 2350 | 0.1165 | - | | 0.0932 | 2400 | 0.1286 | - | | 0.0951 | 2450 | 0.0719 | - | | 0.0971 | 2500 | 0.0562 | - | | 0.0990 | 2550 | 0.123 | - | | 0.1010 | 2600 | 0.0717 | - | | 0.1029 | 2650 | 0.0918 | - | | 0.1049 | 2700 | 0.0756 | - | | 0.1068 | 2750 | 0.0814 | - | | 0.1087 | 2800 | 0.0788 | - | | 0.1107 | 2850 | 0.1019 | - | | 0.1126 | 2900 | 0.1256 | - | | 0.1146 | 2950 | 0.0591 | - | | 0.1165 | 3000 | 0.1196 | - | | 0.1184 | 3050 | 0.0924 | - | | 0.1204 | 3100 | 0.1239 | - | | 0.1223 | 3150 | 0.092 | - | | 0.1243 | 3200 | 0.091 | - | | 0.1262 | 3250 | 0.096 | - | | 0.1282 | 3300 | 0.0944 | - | | 0.1301 | 3350 | 0.0664 | - | | 0.1320 | 3400 | 0.1103 | - | | 0.1340 | 3450 | 0.0584 | - | | 0.1359 | 3500 | 0.0851 | - | | 0.1379 | 3550 | 0.0858 | - | | 0.1398 | 3600 | 0.0886 | - | | 0.1417 | 3650 | 0.1044 | - | | 0.1437 | 3700 | 0.0833 | - | | 0.1456 | 3750 | 0.0582 | - | | 0.1476 | 3800 | 0.0647 | - | | 0.1495 | 3850 | 0.0823 | - | | 0.1515 | 3900 | 0.1256 | - | | 0.1534 | 3950 | 0.0747 | - | | 0.1553 | 4000 | 0.0554 | - | | 0.1573 | 4050 | 0.0969 | - | | 0.1592 | 4100 | 0.0941 | - | | 0.1612 | 4150 | 0.0647 | - | | 0.1631 | 4200 | 0.1008 | - | | 0.1650 | 4250 | 0.0699 | - | | 0.1670 | 4300 | 0.0746 | - | | 0.1689 | 4350 | 0.076 | - | | 0.1709 | 4400 | 0.1026 | - | | 0.1728 | 4450 | 0.0748 | - | | 0.1748 | 4500 | 0.0689 | - | | 0.1767 | 4550 | 0.0675 | - | | 0.1786 | 4600 | 0.0788 | - | | 0.1806 | 4650 | 0.0775 | - | | 0.1825 | 4700 | 0.1021 | - | | 0.1845 | 4750 | 0.0729 | - | | 0.1864 | 4800 | 0.0741 | - | | 0.1883 | 4850 | 0.0494 | - | | 0.1903 | 4900 | 0.0845 | - | | 0.1922 | 4950 | 0.0609 | - | | 0.1942 | 5000 | 0.0548 | - | | 0.1961 | 5050 | 0.0733 | - | | 0.1981 | 5100 | 0.079 | - | | 0.2 | 5150 | 0.0363 | - | | 0.2019 | 5200 | 0.0797 | - | | 0.2039 | 5250 | 0.0754 | - | | 0.2058 | 5300 | 0.0446 | - | | 0.2078 | 5350 | 0.0524 | - | | 0.2097 | 5400 | 0.0715 | - | | 0.2117 | 5450 | 0.0678 | - | | 0.2136 | 5500 | 0.0628 | - | | 0.2155 | 5550 | 0.0554 | - | | 0.2175 | 5600 | 0.0549 | - | | 0.2194 | 5650 | 0.0775 | - | | 0.2214 | 5700 | 0.0558 | - | | 0.2233 | 5750 | 0.0616 | - | | 0.2252 | 5800 | 0.0455 | - | | 0.2272 | 5850 | 0.0538 | - | | 0.2291 | 5900 | 0.0563 | - | | 0.2311 | 5950 | 0.0593 | - | | 0.2330 | 6000 | 0.0688 | - | | 0.2350 | 6050 | 0.0746 | - | | 0.2369 | 6100 | 0.0705 | - | | 0.2388 | 6150 | 0.0517 | - | | 0.2408 | 6200 | 0.0695 | - | | 0.2427 | 6250 | 0.0436 | - | | 0.2447 | 6300 | 0.0409 | - | | 0.2466 | 6350 | 0.0431 | - | | 0.2485 | 6400 | 0.07 | - | | 0.2505 | 6450 | 0.0238 | - | | 0.2524 | 6500 | 0.0825 | - | | 0.2544 | 6550 | 0.0694 | - | | 0.2563 | 6600 | 0.0878 | - | | 0.2583 | 6650 | 0.0462 | - | | 0.2602 | 6700 | 0.0497 | - | | 0.2621 | 6750 | 0.0633 | - | | 0.2641 | 6800 | 0.0653 | - | | 0.2660 | 6850 | 0.0565 | - | | 0.2680 | 6900 | 0.0665 | - | | 0.2699 | 6950 | 0.0842 | - | | 0.2718 | 7000 | 0.0772 | - | | 0.2738 | 7050 | 0.0684 | - | | 0.2757 | 7100 | 0.0723 | - | | 0.2777 | 7150 | 0.0487 | - | | 0.2796 | 7200 | 0.0771 | - | | 0.2816 | 7250 | 0.0397 | - | | 0.2835 | 7300 | 0.0567 | - | | 0.2854 | 7350 | 0.0304 | - | | 0.2874 | 7400 | 0.0421 | - | | 0.2893 | 7450 | 0.0632 | - | | 0.2913 | 7500 | 0.0346 | - | | 0.2932 | 7550 | 0.0255 | - | | 0.2951 | 7600 | 0.0589 | - | | 0.2971 | 7650 | 0.0586 | - | | 0.2990 | 7700 | 0.0617 | - | | 0.3010 | 7750 | 0.0576 | - | | 0.3029 | 7800 | 0.0527 | - | | 0.3049 | 7850 | 0.032 | - | | 0.3068 | 7900 | 0.0315 | - | | 0.3087 | 7950 | 0.038 | - | | 0.3107 | 8000 | 0.0475 | - | | 0.3126 | 8050 | 0.0408 | - | | 0.3146 | 8100 | 0.0457 | - | | 0.3165 | 8150 | 0.0867 | - | | 0.3184 | 8200 | 0.0407 | - | | 0.3204 | 8250 | 0.065 | - | | 0.3223 | 8300 | 0.0547 | - | | 0.3243 | 8350 | 0.0793 | - | | 0.3262 | 8400 | 0.0461 | - | | 0.3282 | 8450 | 0.0808 | - | | 0.3301 | 8500 | 0.0523 | - | | 0.3320 | 8550 | 0.0549 | - | | 0.3340 | 8600 | 0.0607 | - | | 0.3359 | 8650 | 0.0398 | - | | 0.3379 | 8700 | 0.0354 | - | | 0.3398 | 8750 | 0.0865 | - | | 0.3417 | 8800 | 0.0493 | - | | 0.3437 | 8850 | 0.0178 | - | | 0.3456 | 8900 | 0.029 | - | | 0.3476 | 8950 | 0.0402 | - | | 0.3495 | 9000 | 0.0475 | - | | 0.3515 | 9050 | 0.0587 | - | | 0.3534 | 9100 | 0.0591 | - | | 0.3553 | 9150 | 0.0574 | - | | 0.3573 | 9200 | 0.0382 | - | | 0.3592 | 9250 | 0.0358 | - | | 0.3612 | 9300 | 0.032 | - | | 0.3631 | 9350 | 0.0264 | - | | 0.3650 | 9400 | 0.0365 | - | | 0.3670 | 9450 | 0.0366 | - | | 0.3689 | 9500 | 0.0752 | - | | 0.3709 | 9550 | 0.06 | - | | 0.3728 | 9600 | 0.0423 | - | | 0.3748 | 9650 | 0.0388 | - | | 0.3767 | 9700 | 0.0356 | - | | 0.3786 | 9750 | 0.0426 | - | | 0.3806 | 9800 | 0.0404 | - | | 0.3825 | 9850 | 0.0546 | - | | 0.3845 | 9900 | 0.0647 | - | | 0.3864 | 9950 | 0.0335 | - | | 0.3883 | 10000 | 0.0255 | - | | 0.3903 | 10050 | 0.0561 | - | | 0.3922 | 10100 | 0.0221 | - | | 0.3942 | 10150 | 0.0461 | - | | 0.3961 | 10200 | 0.0203 | - | | 0.3981 | 10250 | 0.0484 | - | | 0.4 | 10300 | 0.0429 | - | | 0.4019 | 10350 | 0.0327 | - | | 0.4039 | 10400 | 0.035 | - | | 0.4058 | 10450 | 0.0517 | - | | 0.4078 | 10500 | 0.0209 | - | | 0.4097 | 10550 | 0.0462 | - | | 0.4117 | 10600 | 0.0496 | - | | 0.4136 | 10650 | 0.0135 | - | | 0.4155 | 10700 | 0.0246 | - | | 0.4175 | 10750 | 0.0462 | - | | 0.4194 | 10800 | 0.0334 | - | | 0.4214 | 10850 | 0.0245 | - | | 0.4233 | 10900 | 0.0514 | - | | 0.4252 | 10950 | 0.042 | - | | 0.4272 | 11000 | 0.0395 | - | | 0.4291 | 11050 | 0.0562 | - | | 0.4311 | 11100 | 0.0266 | - | | 0.4330 | 11150 | 0.0485 | - | | 0.4350 | 11200 | 0.0215 | - | | 0.4369 | 11250 | 0.0603 | - | | 0.4388 | 11300 | 0.0285 | - | | 0.4408 | 11350 | 0.0226 | - | | 0.4427 | 11400 | 0.0454 | - | | 0.4447 | 11450 | 0.0375 | - | | 0.4466 | 11500 | 0.0297 | - | | 0.4485 | 11550 | 0.027 | - | | 0.4505 | 11600 | 0.0316 | - | | 0.4524 | 11650 | 0.0361 | - | | 0.4544 | 11700 | 0.0201 | - | | 0.4563 | 11750 | 0.0457 | - | | 0.4583 | 11800 | 0.0328 | - | | 0.4602 | 11850 | 0.0225 | - | | 0.4621 | 11900 | 0.0233 | - | | 0.4641 | 11950 | 0.0212 | - | | 0.4660 | 12000 | 0.0447 | - | | 0.4680 | 12050 | 0.0288 | - | | 0.4699 | 12100 | 0.0185 | - | | 0.4718 | 12150 | 0.029 | - | | 0.4738 | 12200 | 0.0261 | - | | 0.4757 | 12250 | 0.0375 | - | | 0.4777 | 12300 | 0.0366 | - | | 0.4796 | 12350 | 0.026 | - | | 0.4816 | 12400 | 0.0491 | - | | 0.4835 | 12450 | 0.0407 | - | | 0.4854 | 12500 | 0.0168 | - | | 0.4874 | 12550 | 0.0619 | - | | 0.4893 | 12600 | 0.0179 | - | | 0.4913 | 12650 | 0.022 | - | | 0.4932 | 12700 | 0.0331 | - | | 0.4951 | 12750 | 0.0272 | - | | 0.4971 | 12800 | 0.049 | - | | 0.4990 | 12850 | 0.0177 | - | | 0.5010 | 12900 | 0.0484 | - | | 0.5029 | 12950 | 0.0164 | - | | 0.5049 | 13000 | 0.0323 | - | | 0.5068 | 13050 | 0.0213 | - | | 0.5087 | 13100 | 0.0299 | - | | 0.5107 | 13150 | 0.0394 | - | | 0.5126 | 13200 | 0.0759 | - | | 0.5146 | 13250 | 0.0187 | - | | 0.5165 | 13300 | 0.0392 | - | | 0.5184 | 13350 | 0.0361 | - | | 0.5204 | 13400 | 0.0598 | - | | 0.5223 | 13450 | 0.0317 | - | | 0.5243 | 13500 | 0.0343 | - | | 0.5262 | 13550 | 0.0493 | - | | 0.5282 | 13600 | 0.0166 | - | | 0.5301 | 13650 | 0.0454 | - | | 0.5320 | 13700 | 0.0211 | - | | 0.5340 | 13750 | 0.0358 | - | | 0.5359 | 13800 | 0.0416 | - | | 0.5379 | 13850 | 0.0144 | - | | 0.5398 | 13900 | 0.0747 | - | | 0.5417 | 13950 | 0.0186 | - | | 0.5437 | 14000 | 0.0409 | - | | 0.5456 | 14050 | 0.0415 | - | | 0.5476 | 14100 | 0.0057 | - | | 0.5495 | 14150 | 0.0344 | - | | 0.5515 | 14200 | 0.0111 | - | | 0.5534 | 14250 | 0.0322 | - | | 0.5553 | 14300 | 0.0303 | - | | 0.5573 | 14350 | 0.0162 | - | | 0.5592 | 14400 | 0.0304 | - | | 0.5612 | 14450 | 0.049 | - | | 0.5631 | 14500 | 0.0396 | - | | 0.5650 | 14550 | 0.0213 | - | | 0.5670 | 14600 | 0.0157 | - | | 0.5689 | 14650 | 0.0315 | - | | 0.5709 | 14700 | 0.0327 | - | | 0.5728 | 14750 | 0.0303 | - | | 0.5748 | 14800 | 0.0253 | - | | 0.5767 | 14850 | 0.0562 | - | | 0.5786 | 14900 | 0.029 | - | | 0.5806 | 14950 | 0.0177 | - | | 0.5825 | 15000 | 0.0283 | - | | 0.5845 | 15050 | 0.0235 | - | | 0.5864 | 15100 | 0.0194 | - | | 0.5883 | 15150 | 0.0139 | - | | 0.5903 | 15200 | 0.0356 | - | | 0.5922 | 15250 | 0.0339 | - | | 0.5942 | 15300 | 0.0508 | - | | 0.5961 | 15350 | 0.0624 | - | | 0.5981 | 15400 | 0.021 | - | | 0.6 | 15450 | 0.0314 | - | | 0.6019 | 15500 | 0.019 | - | | 0.6039 | 15550 | 0.0105 | - | | 0.6058 | 15600 | 0.0341 | - | | 0.6078 | 15650 | 0.0189 | - | | 0.6097 | 15700 | 0.0414 | - | | 0.6117 | 15750 | 0.0325 | - | | 0.6136 | 15800 | 0.0243 | - | | 0.6155 | 15850 | 0.0277 | - | | 0.6175 | 15900 | 0.0306 | - | | 0.6194 | 15950 | 0.019 | - | | 0.6214 | 16000 | 0.0265 | - | | 0.6233 | 16050 | 0.0312 | - | | 0.6252 | 16100 | 0.0259 | - | | 0.6272 | 16150 | 0.0254 | - | | 0.6291 | 16200 | 0.0375 | - | | 0.6311 | 16250 | 0.0288 | - | | 0.6330 | 16300 | 0.0204 | - | | 0.6350 | 16350 | 0.0257 | - | | 0.6369 | 16400 | 0.0391 | - | | 0.6388 | 16450 | 0.0054 | - | | 0.6408 | 16500 | 0.0388 | - | | 0.6427 | 16550 | 0.0302 | - | | 0.6447 | 16600 | 0.0038 | - | | 0.6466 | 16650 | 0.0427 | - | | 0.6485 | 16700 | 0.0362 | - | | 0.6505 | 16750 | 0.0583 | - | | 0.6524 | 16800 | 0.022 | - | | 0.6544 | 16850 | 0.007 | - | | 0.6563 | 16900 | 0.0469 | - | | 0.6583 | 16950 | 0.0334 | - | | 0.6602 | 17000 | 0.0543 | - | | 0.6621 | 17050 | 0.0373 | - | | 0.6641 | 17100 | 0.0094 | - | | 0.6660 | 17150 | 0.0206 | - | | 0.6680 | 17200 | 0.0353 | - | | 0.6699 | 17250 | 0.0223 | - | | 0.6718 | 17300 | 0.0497 | - | | 0.6738 | 17350 | 0.0368 | - | | 0.6757 | 17400 | 0.0102 | - | | 0.6777 | 17450 | 0.0406 | - | | 0.6796 | 17500 | 0.0596 | - | | 0.6816 | 17550 | 0.0323 | - | | 0.6835 | 17600 | 0.0434 | - | | 0.6854 | 17650 | 0.0248 | - | | 0.6874 | 17700 | 0.0674 | - | | 0.6893 | 17750 | 0.0051 | - | | 0.6913 | 17800 | 0.008 | - | | 0.6932 | 17850 | 0.0379 | - | | 0.6951 | 17900 | 0.0448 | - | | 0.6971 | 17950 | 0.0423 | - | | 0.6990 | 18000 | 0.0357 | - | | 0.7010 | 18050 | 0.0322 | - | | 0.7029 | 18100 | 0.0303 | - | | 0.7049 | 18150 | 0.0351 | - | | 0.7068 | 18200 | 0.0175 | - | | 0.7087 | 18250 | 0.0065 | - | | 0.7107 | 18300 | 0.0359 | - | | 0.7126 | 18350 | 0.0316 | - | | 0.7146 | 18400 | 0.0326 | - | | 0.7165 | 18450 | 0.0095 | - | | 0.7184 | 18500 | 0.0087 | - | | 0.7204 | 18550 | 0.0491 | - | | 0.7223 | 18600 | 0.0285 | - | | 0.7243 | 18650 | 0.0544 | - | | 0.7262 | 18700 | 0.0462 | - | | 0.7282 | 18750 | 0.0276 | - | | 0.7301 | 18800 | 0.0441 | - | | 0.7320 | 18850 | 0.0329 | - | | 0.7340 | 18900 | 0.0686 | - | | 0.7359 | 18950 | 0.0184 | - | | 0.7379 | 19000 | 0.0258 | - | | 0.7398 | 19050 | 0.0055 | - | | 0.7417 | 19100 | 0.0144 | - | | 0.7437 | 19150 | 0.0286 | - | | 0.7456 | 19200 | 0.0132 | - | | 0.7476 | 19250 | 0.0326 | - | | 0.7495 | 19300 | 0.0136 | - | | 0.7515 | 19350 | 0.0299 | - | | 0.7534 | 19400 | 0.0189 | - | | 0.7553 | 19450 | 0.0112 | - | | 0.7573 | 19500 | 0.0566 | - | | 0.7592 | 19550 | 0.0361 | - | | 0.7612 | 19600 | 0.0043 | - | | 0.7631 | 19650 | 0.0455 | - | | 0.7650 | 19700 | 0.0313 | - | | 0.7670 | 19750 | 0.0232 | - | | 0.7689 | 19800 | 0.0235 | - | | 0.7709 | 19850 | 0.0523 | - | | 0.7728 | 19900 | 0.021 | - | | 0.7748 | 19950 | 0.0061 | - | | 0.7767 | 20000 | 0.0292 | - | | 0.7786 | 20050 | 0.0259 | - | | 0.7806 | 20100 | 0.0621 | - | | 0.7825 | 20150 | 0.0367 | - | | 0.7845 | 20200 | 0.0052 | - | | 0.7864 | 20250 | 0.0318 | - | | 0.7883 | 20300 | 0.0053 | - | | 0.7903 | 20350 | 0.019 | - | | 0.7922 | 20400 | 0.0233 | - | | 0.7942 | 20450 | 0.0473 | - | | 0.7961 | 20500 | 0.0058 | - | | 0.7981 | 20550 | 0.0189 | - | | 0.8 | 20600 | 0.0348 | - | | 0.8019 | 20650 | 0.0361 | - | | 0.8039 | 20700 | 0.0205 | - | | 0.8058 | 20750 | 0.0195 | - | | 0.8078 | 20800 | 0.0551 | - | | 0.8097 | 20850 | 0.0178 | - | | 0.8117 | 20900 | 0.0209 | - | | 0.8136 | 20950 | 0.0412 | - | | 0.8155 | 21000 | 0.0352 | - | | 0.8175 | 21050 | 0.0258 | - | | 0.8194 | 21100 | 0.0279 | - | | 0.8214 | 21150 | 0.0143 | - | | 0.8233 | 21200 | 0.0201 | - | | 0.8252 | 21250 | 0.0139 | - | | 0.8272 | 21300 | 0.028 | - | | 0.8291 | 21350 | 0.0078 | - | | 0.8311 | 21400 | 0.0191 | - | | 0.8330 | 21450 | 0.0217 | - | | 0.8350 | 21500 | 0.0384 | - | | 0.8369 | 21550 | 0.0116 | - | | 0.8388 | 21600 | 0.0268 | - | | 0.8408 | 21650 | 0.0225 | - | | 0.8427 | 21700 | 0.0085 | - | | 0.8447 | 21750 | 0.0046 | - | | 0.8466 | 21800 | 0.0235 | - | | 0.8485 | 21850 | 0.0351 | - | | 0.8505 | 21900 | 0.0274 | - | | 0.8524 | 21950 | 0.0201 | - | | 0.8544 | 22000 | 0.0378 | - | | 0.8563 | 22050 | 0.0396 | - | | 0.8583 | 22100 | 0.0454 | - | | 0.8602 | 22150 | 0.0235 | - | | 0.8621 | 22200 | 0.0176 | - | | 0.8641 | 22250 | 0.0281 | - | | 0.8660 | 22300 | 0.0142 | - | | 0.8680 | 22350 | 0.02 | - | | 0.8699 | 22400 | 0.0361 | - | | 0.8718 | 22450 | 0.0189 | - | | 0.8738 | 22500 | 0.046 | - | | 0.8757 | 22550 | 0.0321 | - | | 0.8777 | 22600 | 0.0238 | - | | 0.8796 | 22650 | 0.0246 | - | | 0.8816 | 22700 | 0.0297 | - | | 0.8835 | 22750 | 0.0034 | - | | 0.8854 | 22800 | 0.0335 | - | | 0.8874 | 22850 | 0.0511 | - | | 0.8893 | 22900 | 0.0182 | - | | 0.8913 | 22950 | 0.0214 | - | | 0.8932 | 23000 | 0.0516 | - | | 0.8951 | 23050 | 0.0217 | - | | 0.8971 | 23100 | 0.0128 | - | | 0.8990 | 23150 | 0.0202 | - | | 0.9010 | 23200 | 0.0404 | - | | 0.9029 | 23250 | 0.0162 | - | | 0.9049 | 23300 | 0.0282 | - | | 0.9068 | 23350 | 0.0058 | - | | 0.9087 | 23400 | 0.0326 | - | | 0.9107 | 23450 | 0.0057 | - | | 0.9126 | 23500 | 0.0424 | - | | 0.9146 | 23550 | 0.007 | - | | 0.9165 | 23600 | 0.0217 | - | | 0.9184 | 23650 | 0.0194 | - | | 0.9204 | 23700 | 0.0151 | - | | 0.9223 | 23750 | 0.0325 | - | | 0.9243 | 23800 | 0.0308 | - | | 0.9262 | 23850 | 0.0467 | - | | 0.9282 | 23900 | 0.016 | - | | 0.9301 | 23950 | 0.018 | - | | 0.9320 | 24000 | 0.0369 | - | | 0.9340 | 24050 | 0.0329 | - | | 0.9359 | 24100 | 0.006 | - | | 0.9379 | 24150 | 0.0356 | - | | 0.9398 | 24200 | 0.0053 | - | | 0.9417 | 24250 | 0.0169 | - | | 0.9437 | 24300 | 0.0311 | - | | 0.9456 | 24350 | 0.0045 | - | | 0.9476 | 24400 | 0.0184 | - | | 0.9495 | 24450 | 0.0064 | - | | 0.9515 | 24500 | 0.0722 | - | | 0.9534 | 24550 | 0.0201 | - | | 0.9553 | 24600 | 0.0219 | - | | 0.9573 | 24650 | 0.0286 | - | | 0.9592 | 24700 | 0.005 | - | | 0.9612 | 24750 | 0.0184 | - | | 0.9631 | 24800 | 0.0188 | - | | 0.9650 | 24850 | 0.027 | - | | 0.9670 | 24900 | 0.0325 | - | | 0.9689 | 24950 | 0.0057 | - | | 0.9709 | 25000 | 0.0046 | - | | 0.9728 | 25050 | 0.0155 | - | | 0.9748 | 25100 | 0.0039 | - | | 0.9767 | 25150 | 0.0436 | - | | 0.9786 | 25200 | 0.0434 | - | | 0.9806 | 25250 | 0.0057 | - | | 0.9825 | 25300 | 0.0188 | - | | 0.9845 | 25350 | 0.0069 | - | | 0.9864 | 25400 | 0.0334 | - | | 0.9883 | 25450 | 0.0492 | - | | 0.9903 | 25500 | 0.0126 | - | | 0.9922 | 25550 | 0.084 | - | | 0.9942 | 25600 | 0.033 | - | | 0.9961 | 25650 | 0.0323 | - | | 0.9981 | 25700 | 0.0267 | - | | 1.0 | 25750 | 0.0155 | - | ### Framework Versions - Python: 3.10.12 - SetFit: 1.0.2 - Sentence Transformers: 2.2.2 - Transformers: 4.35.2 - PyTorch: 2.1.0+cu121 - Datasets: 2.16.1 - Tokenizers: 0.15.0 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```