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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: 'Colloqujdi Gio: Lodovico Vives latini, e volgari/Colloqui' |
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- text: Ioannis Lodovici Vivis Von Underweÿsung ayner christlichen Frauwen drey Bücher |
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...erklärt unnd verteütscht. Durch Christophorum Brunonem .../Von Underweysung |
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ayner christlichen Frauwen drey Bücher |
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- text: Absolvtissimae in Hebraicam lingvam institvtiones accvratissime in vsvm studiosæ |
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juuentutis conscriptæ ...Avtore Iohanne Isaaco Leuita Germano/Absolutissimae in |
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Hebraicam linguam institutiones accuratissime in usum studiosæ juventutis conscriptæ |
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... Autore Iohanne Isaaco Levita Germano |
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- text: In tertiam partem D. Thomæ Aqvinatis commentaria Ioannis Wiggers ... a quæstione |
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I. vsque ad quæstionem XXVI. de verbo incarnatoIn tertiam partem D. Thomae Aquinatis |
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commentaria Ioannis Wiggers ... a quaestione I. usque ad quaestionem XXVI. de |
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verbo incarnato |
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- text: Tabvla in grammaticen Hebræam,authore Nicolao Clenardo. A Iohanne Quinquarboreo |
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Aurilacensi à mendis quibus scatebat repurgata, & annotationibus illustrata./Tabula |
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in grammaticen Hebraeam, authore Nicolao Clenardo. A Johanne Quinquarboreo Aurilacensi |
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à mendis quibus scatebat repurgata, & annotationibus illustrata |
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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: 0.735 |
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name: Accuracy |
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--- |
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# SetFit with sentence-transformers/paraphrase-mpnet-base-v2 |
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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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The model has been trained using an efficient few-shot learning technique that involves: |
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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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## Model Details |
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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:** 2 classes |
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<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> |
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<!-- - **Language:** Unknown --> |
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### Model Sources |
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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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### Model Labels |
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| Label | Examples | |
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|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| no | <ul><li>'Exomologesis sive Modus confitendi,per Erasmum Roterodamũ .../Exomologesis sive modus confitendi per Erasmum Roterodamum'</li><li>'Aen-wysinge van de macht en de eer die aen Jesus-Christus toe-komt. En van de eerbiedinghe die-men schuldigh is aen sijn aldersuyverste moeder Maria, en andere heyligen.'</li><li>'Staatkundige vermaningen en voorbeelden, die de deughden en zonden der vorsten betreffen.Nieuwelijks door I.H. Glazemaker vertaalt.'</li></ul> | |
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| yes | <ul><li>'Reclamations des trois états du duché de Brabant sur les atteintes portées a leurs droits et loix constitutionnelles au nom de S.M. Joseph II.'</li><li>'Brief van het Magistraet van Brugge van date 16 February 1788 aen de ordinaire Gedeputeerde der Staeten van Vlaenderen tenderende om staets gewyze te doen naedere Representatie tegen de opregtinge van een Seminarie Generael tot Loven ...'</li><li>"Bericht voor d'Universiteyt &c. van Leuven, over de wijtloopige memorie, en andere schriften en documenten daer by, overgegeven aen haer Ho. Mog. door de vicarissen van Doornik"</li></ul> | |
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## Evaluation |
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### Metrics |
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| Label | Accuracy | |
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|:--------|:---------| |
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| **all** | 0.735 | |
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## Uses |
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### Direct Use for Inference |
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First install the SetFit library: |
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```bash |
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pip install setfit |
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``` |
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Then you can load this model and run inference. |
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```python |
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from setfit import SetFitModel |
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# Download from the 🤗 Hub |
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model = SetFitModel.from_pretrained("yannryanhelsinki/setfit-language-guess") |
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# Run inference |
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preds = model("Colloqujdi Gio: Lodovico Vives latini, e volgari/Colloqui") |
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``` |
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### Downstream Use |
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*List how someone could finetune this model on their own dataset.* |
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### Out-of-Scope Use |
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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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## Bias, Risks and Limitations |
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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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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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## Training Details |
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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 | 5 | 29.2759 | 92 | |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| no | 44 | |
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| yes | 72 | |
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### Training Hyperparameters |
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- batch_size: (16, 16) |
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- num_epochs: (1, 1) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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- num_iterations: 20 |
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- body_learning_rate: (2e-05, 2e-05) |
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- head_learning_rate: 2e-05 |
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- loss: CosineSimilarityLoss |
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- distance_metric: cosine_distance |
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- margin: 0.25 |
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- end_to_end: False |
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- use_amp: False |
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- warmup_proportion: 0.1 |
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- seed: 42 |
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- eval_max_steps: -1 |
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- load_best_model_at_end: False |
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### Training Results |
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| Epoch | Step | Training Loss | Validation Loss | |
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|:------:|:----:|:-------------:|:---------------:| |
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| 0.0034 | 1 | 0.2242 | - | |
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| 0.1724 | 50 | 0.1951 | - | |
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| 0.3448 | 100 | 0.0342 | - | |
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| 0.5172 | 150 | 0.0008 | - | |
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| 0.6897 | 200 | 0.0006 | - | |
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| 0.8621 | 250 | 0.0003 | - | |
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### Framework Versions |
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- Python: 3.10.12 |
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- SetFit: 1.0.3 |
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- Sentence Transformers: 3.0.1 |
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- Transformers: 4.39.0 |
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- PyTorch: 2.3.0+cu121 |
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- Datasets: 2.20.0 |
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- Tokenizers: 0.15.2 |
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## Citation |
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### BibTeX |
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```bibtex |
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@article{https://doi.org/10.48550/arxiv.2209.11055, |
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doi = {10.48550/ARXIV.2209.11055}, |
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url = {https://arxiv.org/abs/2209.11055}, |
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author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, |
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keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
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title = {Efficient Few-Shot Learning Without Prompts}, |
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publisher = {arXiv}, |
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year = {2022}, |
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copyright = {Creative Commons Attribution 4.0 International} |
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} |
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``` |
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