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--- |
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base_model: projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base |
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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: Esteu tots millor callats, no us puc ni veure! |
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- text: Puc canviar el meu idioma preferit? |
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- text: No serveixes per res, és un sistema de merda! |
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- text: Com va tot, com estàs? Quin és l'objecte de la convocatòria de subvencions |
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de l'Ajuntament de Sant Boi de Llobregat? |
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- text: Quin és el millor lloc per comprar un regal? |
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inference: true |
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--- |
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# SetFit with projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base |
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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 [projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base](https://huggingface.co/projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base) 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:** [projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base](https://huggingface.co/projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base) |
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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:** 3 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>'Sou uns fills de puta, no valen res, et feu fora, sou un inútil!'</li><li>'Quin és el seu propòsit?'</li><li>"Aquest text és Ofensiu o fora del domini per a un cercador de tràmits d'un ajuntament"</li></ul> | |
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| 2 | <ul><li>'Ei, què tal? Com va tot?'</li><li>'Bona tarda! Què tal?'</li><li>'Què tal, com va?'</li></ul> | |
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| 0 | <ul><li>"Hola Necessito saber si la modificació no substancial que faré a la meva activitat sotmesa a comunicació prèvia ambiental ha de ser comunicada a l'Ajuntament i no ha de figurar a les actes de control periòdic"</li><li>"Quin és l'objectiu de la Llei 11/2009?"</li><li>'Quin és el benefici de la matrícula?'</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/gret6") |
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# Run inference |
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preds = model("Puc canviar el meu idioma preferit?") |
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``` |
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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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*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 | 1 | 9.3443 | 36 | |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| 0 | 70 | |
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| 1 | 71 | |
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| 2 | 71 | |
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### Training Hyperparameters |
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- batch_size: (64, 64) |
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- num_epochs: (3, 3) |
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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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- l2_weight: 0.01 |
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- seed: 42 |
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- evaluation_strategy: epoch |
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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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| 0.0021 | 1 | 0.1891 | - | |
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| 0.1066 | 50 | 0.1719 | - | |
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| 0.2132 | 100 | 0.0455 | - | |
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| 0.3198 | 150 | 0.0013 | - | |
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| 0.4264 | 200 | 0.0004 | - | |
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| 0.5330 | 250 | 0.0002 | - | |
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| 0.6397 | 300 | 0.0002 | - | |
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| 0.7463 | 350 | 0.0001 | - | |
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| 0.8529 | 400 | 0.0001 | - | |
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| 0.9595 | 450 | 0.0001 | - | |
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| 1.0 | 469 | - | 0.0062 | |
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| 1.0661 | 500 | 0.0001 | - | |
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| 1.1727 | 550 | 0.0001 | - | |
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| 1.2793 | 600 | 0.0001 | - | |
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| 1.3859 | 650 | 0.0001 | - | |
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| 1.4925 | 700 | 0.0001 | - | |
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| 1.5991 | 750 | 0.0001 | - | |
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| 1.7058 | 800 | 0.0001 | - | |
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| 1.8124 | 850 | 0.0001 | - | |
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| 1.9190 | 900 | 0.0001 | - | |
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| 2.0 | 938 | - | 0.0042 | |
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| 2.0256 | 950 | 0.0 | - | |
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| 2.1322 | 1000 | 0.0 | - | |
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| 2.2388 | 1050 | 0.0 | - | |
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| 2.3454 | 1100 | 0.0 | - | |
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| 2.4520 | 1150 | 0.0 | - | |
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| 2.5586 | 1200 | 0.0 | - | |
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| 2.6652 | 1250 | 0.0 | - | |
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| 2.7719 | 1300 | 0.0 | - | |
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| 2.8785 | 1350 | 0.0 | - | |
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| 2.9851 | 1400 | 0.0 | - | |
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| 3.0 | 1407 | - | 0.0034 | |
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### Framework Versions |
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- Python: 3.10.12 |
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- SetFit: 1.1.0 |
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- Sentence Transformers: 3.2.1 |
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- Transformers: 4.42.2 |
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- PyTorch: 2.5.0+cu121 |
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- Datasets: 3.1.0 |
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- Tokenizers: 0.19.1 |
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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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