| --- |
| library_name: setfit |
| tags: |
| - setfit |
| - sentence-transformers |
| - text-classification |
| - generated_from_setfit_trainer |
| metrics: |
| - accuracy |
| widget: |
| - text: considering the use of so-called “fractional citations” in which one divides |
| the number of citations associated with a given paper by the number of authors |
| on that paper [33–38]; |
| - text: Indeed, this is only one of a number of such practical inconsistencies inherent |
| in the traditional h-index; other similar inconsistencies are discussed in Refs. |
| [3, 4]. |
| - text: One of the referees recommends mentioning Quesada (2008) as another characterization |
| of the Hirsch index relying as well on monotonicity. |
| - text: considering the use of so-called “fractional citations” in which one divides |
| the number of citations associated with a given paper by the number of authors |
| on that paper [33–38]; |
| - text: increasing the weighting of very highly-cited papers, either through the introduction |
| of intrinsic weighting factors or the development of entirely new indices which |
| mix the h-index with other more traditional indices (such as total citation count) |
| [3, 4, 7, 8, 26–32]; |
| pipeline_tag: text-classification |
| inference: true |
| base_model: jinaai/jina-embeddings-v2-base-en |
| model-index: |
| - name: SetFit with jinaai/jina-embeddings-v2-base-en |
| results: |
| - task: |
| type: text-classification |
| name: Text Classification |
| dataset: |
| name: Unknown |
| type: unknown |
| split: test |
| metrics: |
| - type: accuracy |
| value: 0.6666666666666666 |
| name: Accuracy |
| --- |
| |
| # SetFit with jinaai/jina-embeddings-v2-base-en |
|
|
| This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [jinaai/jina-embeddings-v2-base-en](https://huggingface.co/jinaai/jina-embeddings-v2-base-en) 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:** [jinaai/jina-embeddings-v2-base-en](https://huggingface.co/jinaai/jina-embeddings-v2-base-en) |
| - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance |
| - **Maximum Sequence Length:** 8192 tokens |
| - **Number of Classes:** 9 classes |
| <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> |
| <!-- - **Language:** Unknown --> |
| <!-- - **License:** Unknown --> |
|
|
| ### 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 | |
| |:-----------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | ccro:BasedOn | <ul><li>'The axiomatizations presented in Quesada (2010, 2011) also dispense with strong monotonicity.'</li></ul> | |
| | ccro:Basedon | <ul><li>'A formal mathematical description of the h-index introduced by Hirsch (2005)'</li><li>'Woeginger (2008a, b) and Quesada (2009, 2010) have already suggested characterizations of the Hirsch index'</li><li>'Woeginger (2008a, b) and Quesada (2009, 2010) have already suggested characterizations of the Hirsch index'</li></ul> | |
| | ccro:Compare | <ul><li>'Instead, a variety of studies [8, 9] have shown that the h index by and large agrees with other objective and subjective measures of scientific quality in a variety of different disciplines (10–15),'</li><li>'Instead, a variety of studies [8, 9] have shown that the h index by and large agrees with other objective and subjective measures of scientific quality in a variety of different disciplines (10–15),'</li><li>'Instead, a variety of studies [8, 9] have shown that the h index by and large agrees with other objective and subjective measures of scientific quality in a variety of different disciplines (10–15),'</li></ul> | |
| | ccro:Contrast | <ul><li>'Hirsch (2005) argues that two individuals with similar Hirsch-index are comparable in terms of their overall scientific impact, even if their total number of papers or their total number of citations is very different.'</li><li>'The three differ from Woeginger’s (2008a) characterization in requiring fewer axioms (three instead of five)'</li><li>'Marchant (2009), instead of characterizing the index itself, characterizes the ranking that the Hirsch index induces on outputs.'</li></ul> | |
| | ccro:Criticize | <ul><li>'The h-index does not take into account that some papers may have extraordinarily many citations, and the g-index tries to compensate for this; see also Egghe (2006b) and Tol (2008).'</li><li>'The h-index does not take into account that some papers may have extraordinarily many citations, and the g-index tries to compensate for this; see also Egghe (2006b) and Tol (2008).'</li><li>'Woeginger (2008a, p. 227) stresses that his axioms should be interpreted within the context of MON.'</li></ul> | |
| | ccro:Discuss | <ul><li>'The relation between N and h will depend on the detailed form of the particular distribution (HI0501-01)'</li><li>'As discussed by Redner (HI0501-03), most papers earn their citations over a limited period of popularity and then they are no longer cited.'</li><li>'It is also possible that papers "drop out" and then later come back into the h count, as would occur for the kind of papers termed "sleeping beauties" (HI0501-04).'</li></ul> | |
| | ccro:Extend | <ul><li>'In [3] the analogous formula for the g-index has been proved'</li></ul> | |
| | ccro:Incorporate | <ul><li>'In this paper, we provide an axiomatic characterization of the Hirsch-index, in very much the same spirit as Arrow (1950, 1951), May (1952), and Moulin (1988) did for numerous other problems in mathematical decision making.'</li><li>'In this paper, we provide an axiomatic characterization of the Hirsch-index, in very much the same spirit as Arrow (1950, 1951), May (1952), and Moulin (1988) did for numerous other problems in mathematical decision making.'</li><li>'In this paper, we provide an axiomatic characterization of the Hirsch-index, in very much the same spirit as Arrow (1950, 1951), May (1952), and Moulin (1988) did for numerous other problems in mathematical decision making.'</li></ul> | |
| | ccro:Negate | <ul><li>'Recently, Lehmann et al. (2, 3) have argued that the mean number of citations per paper (nc = Nc/Np) is a superior indicator.'</li><li>'If one chose instead to use as indicator of scientific achievement the mean number of citations per paper [following Lehmann et al. (2, 3)], our results suggest that (as in the stock market) ‘‘past performance is not predictive of future performance.’’'</li><li>'It has been argued in the literature that one drawback of the h index is that it does not give enough ‘‘credit’’ to very highly cited papers, and various modifications have been proposed to correct this, in particular, Egghe’s g index (4), Jin et al.’s AR index (5), and Komulski’s H(2) index (6).'</li></ul> | |
|
|
| ## Evaluation |
|
|
| ### Metrics |
| | Label | Accuracy | |
| |:--------|:---------| |
| | **all** | 0.6667 | |
|
|
| ## 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/CCRO2") |
| # Run inference |
| preds = model("One of the referees recommends mentioning Quesada (2008) as another characterization of the Hirsch index relying as well on monotonicity.") |
| ``` |
|
|
| <!-- |
| ### Downstream Use |
|
|
| *List how someone could finetune this model on their own dataset.* |
| --> |
|
|
| <!-- |
| ### Out-of-Scope Use |
|
|
| *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| --> |
|
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| <!-- |
| ## Bias, Risks and Limitations |
|
|
| *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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| <!-- |
| ### Recommendations |
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|
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| --> |
|
|
| ## Training Details |
|
|
| ### Training Set Metrics |
| | Training set | Min | Median | Max | |
| |:-------------|:----|:--------|:----| |
| | Word count | 6 | 25.7812 | 53 | |
|
|
| | Label | Training Sample Count | |
| |:-----------------|:----------------------| |
| | ccro:BasedOn | 1 | |
| | ccro:Basedon | 11 | |
| | ccro:Compare | 21 | |
| | ccro:Contrast | 3 | |
| | ccro:Criticize | 4 | |
| | ccro:Discuss | 37 | |
| | ccro:Extend | 1 | |
| | ccro:Incorporate | 14 | |
| | ccro:Negate | 4 | |
|
|
| ### Training Hyperparameters |
| - batch_size: (32, 32) |
| - num_epochs: (1, 1) |
| - max_steps: -1 |
| - sampling_strategy: oversampling |
| - num_iterations: 100 |
| - 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 | |
| |:------:|:----:|:-------------:|:---------------:| |
| | 0.0017 | 1 | 0.311 | - | |
| | 0.0833 | 50 | 0.1338 | - | |
| | 0.1667 | 100 | 0.0054 | - | |
| | 0.25 | 150 | 0.0017 | - | |
| | 0.3333 | 200 | 0.0065 | - | |
| | 0.4167 | 250 | 0.0003 | - | |
| | 0.5 | 300 | 0.0003 | - | |
| | 0.5833 | 350 | 0.0005 | - | |
| | 0.6667 | 400 | 0.0004 | - | |
| | 0.75 | 450 | 0.0002 | - | |
| | 0.8333 | 500 | 0.0002 | - | |
| | 0.9167 | 550 | 0.0002 | - | |
| | 1.0 | 600 | 0.0002 | - | |
| |
| ### Framework Versions |
| - Python: 3.10.12 |
| - SetFit: 1.0.3 |
| - 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} |
| } |
| ``` |
| |
| <!-- |
| ## Glossary |
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