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--- |
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library_name: setfit |
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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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metrics: |
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- accuracy |
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widget: |
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- text: Vem pra Irenil em Paratinga, bonitão |
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- text: Salve Salve Senhor Governador JERÔNIMO RODRIGUES olhando para as TRADIÇÕES |
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- text: Parabéns meu Governador! O foguete 🚀 não para . Muitas realizações entregue |
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em 7 meses , muito trabalho . |
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- text: 👏👏👏 |
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- text: Bom demais governador sobre o piso da enfermagem o que o senhor diz para nos |
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pipeline_tag: text-classification |
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inference: true |
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base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
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model-index: |
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- name: SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-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: 0.9042553191489362 |
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name: Accuracy |
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--- |
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# SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-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-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-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-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-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:** 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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<!-- - **Language:** Unknown --> |
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<!-- - **License:** 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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| Positive | <ul><li>'Enfim,Bonfim 🥳🥳🥳🥳🥳'</li><li>'👏👏👏👏'</li><li>'Pequenas ações fazem sonhos realidades #OhBrabo 💙💙💙'</li></ul> | |
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| Negative | <ul><li>'@jeronimorodriguesba quando terá uma segunda convocação do concurso SECBA?'</li><li>'Cadê a MP do piso da enfermagem ministro'</li><li>'Sim !! A escola municipal aqui do bairro liberdade,30 crianças esperando até hoje as profissionais ADI para crianças que necessita acompanhamento..'</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** | 0.9043 | |
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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("Tarssio/modelo_setfit_politica_BA") |
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# Run inference |
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preds = model("👏👏👏") |
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``` |
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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 | 19.4813 | 313 | |
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| Label | Training Sample Count | |
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|:---------|:----------------------| |
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| Negative | 175 | |
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| Positive | 199 | |
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### Training Hyperparameters |
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- batch_size: (4, 4) |
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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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- num_iterations: 5 |
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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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| 0.0011 | 1 | 0.3616 | - | |
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| 0.0535 | 50 | 0.3129 | - | |
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| 0.1070 | 100 | 0.2912 | - | |
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| 0.1604 | 150 | 0.191 | - | |
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| 0.2139 | 200 | 0.0907 | - | |
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| 0.2674 | 250 | 0.0086 | - | |
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| 0.3209 | 300 | 0.0042 | - | |
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| 0.3743 | 350 | 0.0161 | - | |
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| 0.4278 | 400 | 0.0007 | - | |
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| 0.4813 | 450 | 0.0403 | - | |
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| 0.5348 | 500 | 0.0055 | - | |
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| 0.5882 | 550 | 0.0057 | - | |
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| 0.6417 | 600 | 0.0002 | - | |
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| 0.6952 | 650 | 0.0002 | - | |
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| 0.7487 | 700 | 0.0 | - | |
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| 0.8021 | 750 | 0.0026 | - | |
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| 0.8556 | 800 | 0.0002 | - | |
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| 0.9091 | 850 | 0.0002 | - | |
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| 0.9626 | 900 | 0.0004 | - | |
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| 1.0 | 935 | - | 0.1724 | |
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| 1.0160 | 950 | 0.0001 | - | |
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| 1.0695 | 1000 | 0.0006 | - | |
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| 1.1230 | 1050 | 0.0001 | - | |
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| 1.1765 | 1100 | 0.0008 | - | |
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| 1.2299 | 1150 | 0.0002 | - | |
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| 1.2834 | 1200 | 0.0001 | - | |
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| 1.3369 | 1250 | 0.0002 | - | |
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| 1.3904 | 1300 | 0.0002 | - | |
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| 1.4439 | 1350 | 0.0002 | - | |
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| 1.4973 | 1400 | 0.0002 | - | |
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| 1.5508 | 1450 | 0.0 | - | |
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| 1.6043 | 1500 | 0.0002 | - | |
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| 1.6578 | 1550 | 0.2178 | - | |
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| 1.7112 | 1600 | 0.0002 | - | |
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| 1.7647 | 1650 | 0.0001 | - | |
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| 1.8182 | 1700 | 0.0001 | - | |
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| 1.8717 | 1750 | 0.0003 | - | |
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| 1.9251 | 1800 | 0.0359 | - | |
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| 1.9786 | 1850 | 0.0001 | - | |
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| 2.0 | 1870 | - | 0.1601 | |
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| 2.0321 | 1900 | 0.0001 | - | |
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| 2.0856 | 1950 | 0.0002 | - | |
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| 2.1390 | 2000 | 0.0001 | - | |
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| 2.1925 | 2050 | 0.0001 | - | |
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| 2.2460 | 2100 | 0.0002 | - | |
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| 2.2995 | 2150 | 0.0002 | - | |
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| 2.3529 | 2200 | 0.0003 | - | |
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| 2.4064 | 2250 | 0.0001 | - | |
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| 2.4599 | 2300 | 0.0002 | - | |
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| 2.5134 | 2350 | 0.0001 | - | |
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| 2.5668 | 2400 | 0.0 | - | |
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| 2.6203 | 2450 | 0.0001 | - | |
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| 2.6738 | 2500 | 0.0 | - | |
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| 2.7273 | 2550 | 0.0001 | - | |
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| 2.7807 | 2600 | 0.0001 | - | |
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| 2.8342 | 2650 | 0.0 | - | |
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| 2.8877 | 2700 | 0.0 | - | |
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| 2.9412 | 2750 | 0.0 | - | |
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| 2.9947 | 2800 | 0.0001 | - | |
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| **3.0** | **2805** | **-** | **0.1568** | |
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| 3.0481 | 2850 | 0.0001 | - | |
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| 3.1016 | 2900 | 0.0001 | - | |
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| 3.1551 | 2950 | 0.0001 | - | |
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| 3.2086 | 3000 | 0.0001 | - | |
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| 3.2620 | 3050 | 0.0001 | - | |
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| 3.3155 | 3100 | 0.0045 | - | |
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| 3.3690 | 3150 | 0.0 | - | |
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| 3.4225 | 3200 | 0.0001 | - | |
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| 3.4759 | 3250 | 0.0002 | - | |
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| 3.5294 | 3300 | 0.0 | - | |
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| 3.5829 | 3350 | 0.0002 | - | |
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| 3.6364 | 3400 | 0.0 | - | |
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| 3.6898 | 3450 | 0.0 | - | |
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| 3.7433 | 3500 | 0.0002 | - | |
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| 3.7968 | 3550 | 0.0 | - | |
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| 3.8503 | 3600 | 0.0 | - | |
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| 3.9037 | 3650 | 0.0005 | - | |
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| 3.9572 | 3700 | 0.0001 | - | |
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| 4.0 | 3740 | - | 0.1574 | |
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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: 2.2.2 |
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- Transformers: 4.35.2 |
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- PyTorch: 2.1.0+cu121 |
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- Datasets: 2.16.1 |
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- Tokenizers: 0.15.0 |
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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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