--- base_model: sentence-transformers/all-roberta-large-v1 library_name: setfit metrics: - accuracy pipeline_tag: text-classification tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: ' Portal:Georgia: Portal:Georgia It seems the day of the disambiguation portal has been and gone. No further use as we can and do use mainspace disambig pages now. BRS (Talk) (Contribs) 13:57, 4 May 2014 (UTC) [ reply ] Comments Votes Remove Somehow we''ve recently fallen out of the very-long-standing habit of putting these deletion nums under sections by date. I suspect this is a matter of not having a tool that helps with the technical operation of adding noms (I hope eventually to have such assistance for creating, closing, and archiving various kinds of noms, keeping in mind there are technical idiosyncrasies to particular kinds of noms). -- Pi zero ( talk ) 14:29, 4 May 2014 (UTC) [ reply ] Comment There''s a framework in place for migrating to subpages (which would semi-automate all the opening and closing and archiving and {{ votings }} and things), but that''s something for the talk page. Date sections make less sense when there''s very few deletion proposals each week. Microchip08 ( talk ) 07:49, 6 May 2014 (UTC) [ reply ] They''re handy for keeping track of when noms took place, which is useful all around. And taking it all to the talk page seems needlessly bureaucratic given, as you say, the low traffic involved. -- Pi zero ( talk ) 12:40, 6 May 2014 (UTC) [ reply ] Keep ; it does no harm, especially as the mainspace Georgia disambiguates between categories and not these portals. This only really needs to go if one of the Georgia portals gets deleted; at the moment it''s serving a useful purpose. Microchip08 ( talk ) 07:49, 6 May 2014 (UTC) [ reply ] Comment Portals as an entire class of pages on en.wn are deprecated in favor of categories. -- Pi zero ( talk ) 12:40, 6 May 2014 (UTC) [ reply ] The above discussion is preserved as an archive. Please do not modify it. Subsequent comments should be made on the appropriate discussion page (such as this page''s talk page , admin action alerts or the talk page of the nominated article). No further edits should be made to this page. ' - text: ' User:Azhar sabri: I''m not sure why this DR was opened as there was a speedy deletion request on the page, which I''ve now actioned. BRS (Talk) (Contribs) 16:40, 13 April 2014 (UTC) [ reply ] User:Azhar sabri In my opinion, this seems like inappropriate use of the userspace based on my observations found here and here where there seemed to be a widespread "spam" attack of sorts. I''ve tried to warn the user to stick to the missions of English Wikinews, but I''m not sure if it''s an appropriate attempt at communication or not given the circumstances, so I would like the community here to review that too. TeleComNasSprVen ( talk ) 20:20, 6 March 2014 (UTC) [ reply ] Votes The above discussion is preserved as an archive. Please do not modify it. Subsequent comments should be made on the appropriate discussion page (such as this page''s talk page , admin action alerts or the talk page of the nominated article). No further edits should be made to this page. ' - text: ' Interviews: Interviews WN:Story preparation/Office hours:Frank Schulenberg Last edited in December 2009. Highly unlikely to be published now. WN:Story preparation/Wikinews Interviews: Jim McGreevey Last edited in September 2007. Highly unlikely to be published now. These have been put together because they are unpublished interviews from several years ago. -- Green Giant ( talk ) 17:54, 16 April 2020 (UTC) [ reply ] Comments Votes The above discussion is preserved as an archive. Please do not modify it. Subsequent comments should be made on the appropriate discussion page (such as this page''s talk page , admin action alerts or the talk page of the nominated article). No further edits should be made to this page. ' - text: ' AZOperator: Files uploaded by AZOperator ( t · c · b ) for use in US: Judge enters ''not guilty'' for Florida school shooting suspect, audio/video forced release with FBI timeline File:Nikolas Cruz campus movements.png File:Suspect Movement in Building 12.png It is my opinion, these images do not qualify for a fair use exception with regards to reusing copyrighted images. The uploader has stated they were found on Commons , however, if they were there, I cannot locate them now. The aspect of the story they are used to illustrate is by no means ''breaking news'' furthermore it would be easy to create free versions by anyone with a graphics program, a free satellite photo, and a few hours of spare time. As best as I can determine, the images are the work of the Sun-Sentinel as seen in this article . Thank you for your time, -- SVT Cobra 19:26, 17 March 2018 (UTC) [ reply ] Comments Hello SVTCobra. "a few hours of spare time" is a wonderful description of one of the resources that journalistic work requires. :-) Concur that the images may be reproduced by hand to convey the same information (or even more) in a free way. -- Gryllida ( talk ) 02:41, 19 March 2018 (UTC) [ reply ] Votes Remove as argued. Article works well without these images and shouldn''t contain non-free images. -- Gwyndon ( talk ) 02:33, 19 March 2018 (UTC) [ reply ] Remove per my own nomination. -- SVT Cobra 14:09, 22 March 2018 (UTC) [ reply ] Remove per nomination and because the article has been deleted. Green Giant ( talk ) 22:05, 10 April 2018 (UTC) [ reply ] Remove as per nominator. The article does not exist. Harsh Rathod Poke me! 04:46, 25 June 2018 (UTC) [ reply ] The above discussion is preserved as an archive. Please do not modify it. Subsequent comments should be made on the appropriate discussion page (such as this page''s talk page , admin action alerts or the talk page of the nominated article). No further edits should be made to this page. ' - text: ' Userspace drafts: Userspace drafts I’ve come across a large number of draft articles in userspace. Most seem to be very stale but do not fit into a speedy deletion criterion. I’m listing them here in batches for community scrutiny. The first batch are all in anonymous IP userspace: User:194.19.60.4/Norwegian Broadcasting (P3) broadcast story about Muhammed as pig Last edit was February 2006. User:194.247.230.196/Wikipedia is islamophobic Last edit was March 2006. User:202.69.163.8/Philippines Last edit was March 2008. User:210.211.194.223/HAS HUMANITY A FUTURE Last edit was February 2006. User:41.204.73.49/Cameroon 2011 presidential election Last edit was October 2011. User:66.65.46.8/Reaction to Danish Cartoons Last edit was February 2006. Thank you for reading this far. -- Green Giant ( talk ) 18:14, 30 July 2021 (UTC) [ reply ] Comments Votes Delete - as nominator. -- Green Giant ( talk ) 18:14, 30 July 2021 (UTC) [ reply ] No objections in two weeks. The result was to delete these pages. -- Cromium ( talk ) 01:26, 13 August 2021 (UTC) [ reply ] The above discussion is preserved as an archive . Please do not modify it. Subsequent comments should be made on the appropriate discussion page, such as the current discussion page. No further edits should be made to this discussion. ' inference: true --- # SetFit with sentence-transformers/all-roberta-large-v1 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) 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-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) - **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:** 4 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 | 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| 1 | | | 2 | | | 3 | | | 0 | | ## 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("research-dump/all-roberta-large-v1_wikinews_outcome_prediction_v1") # Run inference preds = model(" Interviews: Interviews WN:Story preparation/Office hours:Frank Schulenberg Last edited in December 2009. Highly unlikely to be published now. WN:Story preparation/Wikinews Interviews: Jim McGreevey Last edited in September 2007. Highly unlikely to be published now. These have been put together because they are unpublished interviews from several years ago. -- Green Giant ( talk ) 17:54, 16 April 2020 (UTC) [ reply ] Comments Votes The above discussion is preserved as an archive. Please do not modify it. Subsequent comments should be made on the appropriate discussion page (such as this page's talk page , admin action alerts or the talk page of the nominated article). No further edits should be made to this page. ") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:---------|:-----| | Word count | 60 | 404.1905 | 3700 | | Label | Training Sample Count | |:------|:----------------------| | 0 | 4 | | 1 | 37 | | 2 | 15 | | 3 | 7 | ### Training Hyperparameters - batch_size: (4, 2) - num_epochs: (1, 16) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 100 - body_learning_rate: (1e-05, 1e-05) - head_learning_rate: 5e-05 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: True - use_amp: True - warmup_proportion: 0.1 - l2_weight: 0.01 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:------:|:----:|:-------------:|:---------------:| | 0.0003 | 1 | 0.446 | - | | 0.0317 | 100 | 0.2329 | 0.1799 | | 0.0635 | 200 | 0.1037 | 0.1821 | | 0.0952 | 300 | 0.0326 | 0.2931 | | 0.1270 | 400 | 0.0248 | 0.4236 | | 0.1587 | 500 | 0.012 | 0.2775 | | 0.1905 | 600 | 0.0047 | 0.3502 | | 0.2222 | 700 | 0.0018 | 0.2837 | | 0.2540 | 800 | 0.0002 | 0.2931 | | 0.2857 | 900 | 0.0005 | 0.2921 | | 0.3175 | 1000 | 0.0001 | 0.2948 | | 0.3492 | 1100 | 0.0001 | 0.2968 | | 0.3810 | 1200 | 0.0001 | 0.2950 | | 0.4127 | 1300 | 0.0001 | 0.2962 | | 0.4444 | 1400 | 0.0 | 0.2953 | | 0.4762 | 1500 | 0.0 | 0.2971 | | 0.5079 | 1600 | 0.0 | 0.2970 | | 0.5397 | 1700 | 0.0 | 0.2980 | | 0.5714 | 1800 | 0.0 | 0.2973 | | 0.6032 | 1900 | 0.0 | 0.2966 | | 0.6349 | 2000 | 0.0 | 0.2973 | | 0.6667 | 2100 | 0.0 | 0.2990 | | 0.6984 | 2200 | 0.0 | 0.2984 | | 0.7302 | 2300 | 0.0 | 0.3001 | | 0.7619 | 2400 | 0.0 | 0.2997 | | 0.7937 | 2500 | 0.0 | 0.2994 | | 0.8254 | 2600 | 0.0 | 0.2980 | | 0.8571 | 2700 | 0.0 | 0.2985 | | 0.8889 | 2800 | 0.0 | 0.2987 | | 0.9206 | 2900 | 0.0 | 0.2969 | | 0.9524 | 3000 | 0.0 | 0.2969 | | 0.9841 | 3100 | 0.0 | 0.2969 | ### Framework Versions - Python: 3.10.12 - SetFit: 1.1.0 - Sentence Transformers: 3.3.1 - Transformers: 4.44.1 - PyTorch: 2.2.1+cu121 - Datasets: 2.21.0 - Tokenizers: 0.19.1 ## 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} } ```