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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: ' i still dont know what we would do though' |
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- text: ' where`d you go!' |
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- text: ' Thank you! I`m working on `s' |
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- text: Terminator Salvation... by myself. |
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- text: ' lol man i got 2 1 /2 hrs an iont how i woulda made it wit out my ramen noodles |
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and t.v. Time' |
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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: 0.79 |
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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:** 3 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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| 0 | <ul><li>'چه سودایی که سر همینا از دست دادم😂'</li><li>'خو فارسی بنویس بفهمه 😂😂😂😂😂'</li><li>'اینجا ایران همین سایتا هم\u200cزیادی..نیازی به بررسی ندارن...کلا دوسداریم به همچی ایراد بگیریم.'</li></ul> | |
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| 1 | <ul><li>'کد کارت مشکی NHKDKI'</li><li>'اتفاقا مسیولیت بیشتری برات میاره و درگیریات بیشتر میشه برای هدفی که داری'</li><li>'من میخام شروع کنم،اورج بفروشم یا فیک؟فیک ارزونتره ولی فیکه.اورجینال هم ک گرون تره ؟بنظرت اورج میخرن؟؟'</li></ul> | |
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| 2 | <ul><li>'🔥🔥🔥🔥'</li><li>'😂😂😂'</li><li>'چه قدر عالی وخفن 🔥🔥'</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** | 0.79 | |
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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("ehsanhallo/setfit-paraphrase-multilingual-MiniLM-L12-v2-ig-fa") |
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# Run inference |
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preds = model(" where`d you go!") |
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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 | 6.4184 | 75 | |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| 0 | 69 | |
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| 1 | 238 | |
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| 2 | 551 | |
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### Training Hyperparameters |
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- batch_size: (32, 16) |
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- num_epochs: (1, 2) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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- body_learning_rate: (2e-05, 5e-06) |
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- head_learning_rate: 0.002 |
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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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- 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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| 0.0001 | 1 | 0.1767 | - | |
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| 0.0216 | 250 | 0.1513 | - | |
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| 0.0431 | 500 | 0.0629 | 0.2389 | |
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| 0.0647 | 750 | 0.0351 | - | |
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| 0.0862 | 1000 | 0.0015 | 0.1886 | |
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| 0.1078 | 1250 | 0.0003 | - | |
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| 0.1293 | 1500 | 0.0004 | 0.1813 | |
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| 0.1509 | 1750 | 0.0002 | - | |
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| **0.1724** | **2000** | **0.0002** | **0.1807** | |
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| 0.1940 | 2250 | 0.0001 | - | |
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| 0.2155 | 2500 | 0.0001 | 0.187 | |
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| 0.2371 | 2750 | 0.0001 | - | |
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| 0.2586 | 3000 | 0.0001 | 0.1903 | |
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| 0.2802 | 3250 | 0.0001 | - | |
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| 0.3018 | 3500 | 0.0 | 0.1864 | |
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| 0.3233 | 3750 | 0.0 | - | |
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| 0.3449 | 4000 | 0.0 | 0.193 | |
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| 0.3664 | 4250 | 0.0 | - | |
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| 0.3880 | 4500 | 0.0 | 0.1879 | |
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| 0.4095 | 4750 | 0.0 | - | |
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| 0.4311 | 5000 | 0.0 | 0.1887 | |
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| 0.4526 | 5250 | 0.0 | - | |
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| 0.4742 | 5500 | 0.0 | 0.187 | |
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| 0.4957 | 5750 | 0.0 | - | |
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| 0.5173 | 6000 | 0.0001 | 0.205 | |
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| 0.5388 | 6250 | 0.0 | - | |
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| 0.5604 | 6500 | 0.0 | 0.205 | |
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| 0.5819 | 6750 | 0.0 | - | |
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| 0.6035 | 7000 | 0.0 | 0.2018 | |
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| 0.6251 | 7250 | 0.0 | - | |
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| 0.6466 | 7500 | 0.0 | 0.2022 | |
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| 0.6682 | 7750 | 0.0 | - | |
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| 0.6897 | 8000 | 0.0 | 0.2063 | |
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| 0.7113 | 8250 | 0.0 | - | |
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| 0.7328 | 8500 | 0.0 | 0.2143 | |
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| 0.7544 | 8750 | 0.0 | - | |
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| 0.7759 | 9000 | 0.0 | 0.2206 | |
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| 0.7975 | 9250 | 0.0 | - | |
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| 0.8190 | 9500 | 0.0 | 0.2167 | |
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| 0.8406 | 9750 | 0.0 | - | |
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| 0.8621 | 10000 | 0.0 | 0.2176 | |
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| 0.8837 | 10250 | 0.0 | - | |
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| 0.9053 | 10500 | 0.0 | 0.217 | |
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| 0.9268 | 10750 | 0.0 | - | |
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| 0.9484 | 11000 | 0.0 | 0.2153 | |
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| 0.9699 | 11250 | 0.0 | - | |
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| 0.9915 | 11500 | 0.0 | 0.2137 | |
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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: 1.0.1 |
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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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