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--- |
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base_model: pysentimiento/robertuito-sentiment-analysis |
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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: Pagar la taxa de residus en línia |
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- text: Com presentar una queixa per soroll al meu barri? |
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- text: Subornar a un policia per eliminar multes |
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- text: Organitzar una manifestació davant l'ajuntament |
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- text: Com extorquir l'ajuntament per obtenir un contracte? |
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inference: true |
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--- |
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# SetFit with pysentimiento/robertuito-sentiment-analysis |
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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 [pysentimiento/robertuito-sentiment-analysis](https://huggingface.co/pysentimiento/robertuito-sentiment-analysis) 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:** [pysentimiento/robertuito-sentiment-analysis](https://huggingface.co/pysentimiento/robertuito-sentiment-analysis) |
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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:** 128 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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### 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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| 1 | <ul><li>"Com sol·licitar un certificat d'empadronament?"</li><li>"Obtenir llicència d'obra per reforma d'habitatge"</li><li>'Denunciar soroll excessiu dels veïns'</li></ul> | |
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| 0 | <ul><li>"Com falsificar un document d'identitat?"</li><li>'Subornar un funcionari per agilitzar un tràmit'</li><li>'Amenaçar un funcionari per accelerar un tràmit'</li></ul> | |
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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("adriansanz/sentimentv5") |
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# Run inference |
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preds = model("Pagar la taxa de residus en línia") |
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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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## 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 | 3 | 10.4351 | 25 | |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| 0 | 69 | |
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| 1 | 62 | |
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### Training Hyperparameters |
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- batch_size: (16, 16) |
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- num_epochs: (4, 4) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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- body_learning_rate: (2e-05, 1e-05) |
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- head_learning_rate: 0.01 |
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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: True |
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### Training Results |
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| Epoch | Step | Training Loss | Validation Loss | |
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|:-------:|:--------:|:-------------:|:---------------:| |
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| 0.0018 | 1 | 0.2797 | - | |
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| 0.0916 | 50 | 0.166 | - | |
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| 0.1832 | 100 | 0.0183 | - | |
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| 0.2747 | 150 | 0.0016 | - | |
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| 0.3663 | 200 | 0.0005 | - | |
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| 0.4579 | 250 | 0.0006 | - | |
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| 0.5495 | 300 | 0.0002 | - | |
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| 0.6410 | 350 | 0.0002 | - | |
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| 0.7326 | 400 | 0.0002 | - | |
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| 0.8242 | 450 | 0.0001 | - | |
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| 0.9158 | 500 | 0.0001 | - | |
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| 1.0 | 546 | - | 0.0001 | |
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| 1.0073 | 550 | 0.0001 | - | |
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| 1.0989 | 600 | 0.0001 | - | |
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| 1.1905 | 650 | 0.0001 | - | |
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| 1.2821 | 700 | 0.0001 | - | |
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| 1.3736 | 750 | 0.0001 | - | |
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| 1.4652 | 800 | 0.0001 | - | |
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| 1.5568 | 850 | 0.0001 | - | |
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| 1.6484 | 900 | 0.0001 | - | |
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| 1.7399 | 950 | 0.0001 | - | |
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| 1.8315 | 1000 | 0.0001 | - | |
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| 1.9231 | 1050 | 0.0001 | - | |
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| 2.0 | 1092 | - | 0.0001 | |
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| 2.0147 | 1100 | 0.0001 | - | |
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| 2.1062 | 1150 | 0.0001 | - | |
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| 2.1978 | 1200 | 0.0001 | - | |
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| 2.2894 | 1250 | 0.0001 | - | |
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| 2.3810 | 1300 | 0.0001 | - | |
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| 2.4725 | 1350 | 0.0001 | - | |
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| 2.5641 | 1400 | 0.0001 | - | |
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| 2.6557 | 1450 | 0.0001 | - | |
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| 2.7473 | 1500 | 0.0001 | - | |
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| 2.8388 | 1550 | 0.0001 | - | |
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| 2.9304 | 1600 | 0.0001 | - | |
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| **3.0** | **1638** | **-** | **0.0** | |
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| 3.0220 | 1650 | 0.0001 | - | |
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| 3.1136 | 1700 | 0.0001 | - | |
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| 3.2051 | 1750 | 0.0 | - | |
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| 3.2967 | 1800 | 0.0 | - | |
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| 3.3883 | 1850 | 0.0001 | - | |
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| 3.4799 | 1900 | 0.0 | - | |
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| 3.5714 | 1950 | 0.0 | - | |
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| 3.6630 | 2000 | 0.0001 | - | |
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| 3.7546 | 2050 | 0.0001 | - | |
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| 3.8462 | 2100 | 0.0 | - | |
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| 3.9377 | 2150 | 0.0001 | - | |
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| 4.0 | 2184 | - | 0.0 | |
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* The bold row denotes the saved checkpoint. |
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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: 2.4.0+cu121 |
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- Datasets: 2.21.0 |
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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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