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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: J'avais comme habitudes de manger en regardant un film 🎥 et là j'ai arrêté |
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ça . Je ne regarde plus de films car mon cerveau à relié films = nourriture . |
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C'est fou non ! |
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- text: 'Surtout quand tu es un jeune homme, commencer à se forger un physique c''est |
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important. |
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Rester maigre qqpart dans le regard des autres c''est rester un enfant, j''ai |
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connu c''est vraiment pas agréable' |
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- text: 'typique de la société capitaliste/consumériste = remplacer le problème par |
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un autre problème. Je suis certaine qu''il y a d''autres troubles dont on ne parle |
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pas. |
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Moi qui aies connu tout l''inverse (anorexie) les médias ont été en grande partie |
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la cause. On en a pas fini avec tous ces troubles qui se développent dans la société |
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qui est la notre.' |
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- text: 'C’est horrible. |
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L’anorexie est une maladie mentale terrible et elle fait énormément souffrir, |
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surtout psychologiquement que physiquement, c’est invivable.. |
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Je suis contente qu’elle se sente mieux aujourd’hui, mais ce qu’elle a vécu il |
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y a quelques temps n’est pas une vie, 90 laxatifs par jour, sa raison de vivre |
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était de perdre du poids, une raison qu’il l’a énormément rapproché de la mort. |
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C’est une maladie avant tout mentale, pour essayer de la guérir au mieux ils font |
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tomber sur une hospitalisation où le personnel soignant et les médecins ne chercheront |
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pas à goinfrer la fille, ou le garçon. |
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À longs therme et court therme les conséquences de la maladie peuvent être très |
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néfastes, on peut perdre un organe, on peut mourir, il peut y avoir des troubles |
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obsessionnels compulsifs, des idées suicidaires, et j’en passe... |
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J’envoie ma force à ces filles qui a l’heure où j’écris sont hospitalisées, sont |
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souffrantes de ça, je sais que vous dire de vous battre n’est pas forcément efficace, |
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seul vous pouvez changer votre point de vue, un jour vous y arriverez, je sais |
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que vous n’y croyez pas, mais moi pourtant j’y crois..' |
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- text: Lire "l'anorexie une addiction au plaisir de maigrir" sciences et avenir |
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pipeline_tag: text-classification |
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inference: false |
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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: 0.7860696517412935 |
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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 OneVsRestClassifier 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 OneVsRestClassifier instance |
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- **Maximum Sequence Length:** 128 tokens |
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<!-- - **Number of Classes:** Unknown --> |
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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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## Evaluation |
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### Metrics |
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| Label | Accuracy | |
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|:--------|:---------| |
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| **all** | 0.7861 | |
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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("Ghofranem/setfit-paraphrase-multilingual-MiniLM-L12-v2-ed-fr") |
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# Run inference |
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preds = model("Lire \"l'anorexie une addiction au plaisir de maigrir\" sciences et avenir") |
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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 | 4 | 68.8313 | 694 | |
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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: 5 |
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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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| 0.0010 | 1 | 0.3025 | - | |
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| 0.0498 | 50 | 0.3227 | - | |
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| 0.0996 | 100 | 0.1451 | - | |
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| 0.1494 | 150 | 0.0662 | - | |
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| 0.1992 | 200 | 0.1114 | - | |
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| 0.2490 | 250 | 0.0723 | - | |
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| 0.2988 | 300 | 0.0375 | - | |
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| 0.3486 | 350 | 0.0252 | - | |
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| 0.3984 | 400 | 0.0497 | - | |
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| 0.4482 | 450 | 0.087 | - | |
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| 0.4980 | 500 | 0.0584 | - | |
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| 0.5478 | 550 | 0.0758 | - | |
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| 0.5976 | 600 | 0.0624 | - | |
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| 0.6474 | 650 | 0.0572 | - | |
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| 0.6972 | 700 | 0.0726 | - | |
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| 0.7470 | 750 | 0.0012 | - | |
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| 0.7968 | 800 | 0.0052 | - | |
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| 0.8466 | 850 | 0.0309 | - | |
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| 0.8964 | 900 | 0.0713 | - | |
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| 0.9462 | 950 | 0.0043 | - | |
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| 0.9960 | 1000 | 0.0049 | - | |
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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: 2.5.1 |
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- Transformers: 4.38.2 |
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- PyTorch: 2.2.1+cu121 |
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- Datasets: 2.18.0 |
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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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