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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: παστα ατομικη |
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- text: mikel mini croissant σοκολατα |
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- text: tasty nat nut παστελι σουσαμι |
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- text: σκιουφιχτα σαλτσα ντοματας μυζηθρα σαλτσα ντοματας ελιες καππαρη μυζηθρα |
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- text: κρασι ροζε λιανος |
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pipeline_tag: text-classification |
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inference: false |
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base_model: lighteternal/stsb-xlm-r-greek-transfer |
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model-index: |
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- name: SetFit with lighteternal/stsb-xlm-r-greek-transfer |
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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: 0.1588785046728972 |
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name: Accuracy |
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--- |
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# SetFit with lighteternal/stsb-xlm-r-greek-transfer |
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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 [lighteternal/stsb-xlm-r-greek-transfer](https://huggingface.co/lighteternal/stsb-xlm-r-greek-transfer) 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:** [lighteternal/stsb-xlm-r-greek-transfer](https://huggingface.co/lighteternal/stsb-xlm-r-greek-transfer) |
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- **Classification head:** a OneVsRestClassifier instance |
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- **Maximum Sequence Length:** 400 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** | 0.1589 | |
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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("st-karlos-efood/setfit-multilabel-one-vs-rest-feb-2024") |
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# Run inference |
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preds = model("παστα ατομικη") |
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``` |
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<!-- |
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### Downstream Use |
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*List how someone could finetune this model on their own dataset.* |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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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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### Recommendations |
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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 | 8.6048 | 116 | |
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### Training Hyperparameters |
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- batch_size: (48, 48) |
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- num_epochs: (5, 5) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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- num_iterations: 10 |
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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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| 0.0008 | 1 | 0.2009 | - | |
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| 0.0377 | 50 | 0.1674 | - | |
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| 0.0754 | 100 | 0.1593 | - | |
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| 0.1131 | 150 | 0.1793 | - | |
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| 0.1508 | 200 | 0.176 | - | |
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| 0.1885 | 250 | 0.1818 | - | |
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| 0.2262 | 300 | 0.1209 | - | |
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| 0.2640 | 350 | 0.1546 | - | |
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| 0.3017 | 400 | 0.0996 | - | |
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| 0.3394 | 450 | 0.1108 | - | |
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| 0.3771 | 500 | 0.1163 | - | |
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| 0.4148 | 550 | 0.1102 | - | |
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| 0.4525 | 600 | 0.1477 | - | |
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| 0.4902 | 650 | 0.0973 | - | |
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| 0.5279 | 700 | 0.1324 | - | |
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| 0.5656 | 750 | 0.1792 | - | |
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| 0.6033 | 800 | 0.1026 | - | |
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| 0.6410 | 850 | 0.1461 | - | |
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| 0.6787 | 900 | 0.117 | - | |
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| 0.7164 | 950 | 0.0907 | - | |
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| 0.7541 | 1000 | 0.0904 | - | |
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| 0.7919 | 1050 | 0.1168 | - | |
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| 0.8296 | 1100 | 0.0831 | - | |
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| 0.8673 | 1150 | 0.0623 | - | |
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| 0.9050 | 1200 | 0.0802 | - | |
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| 0.9427 | 1250 | 0.0802 | - | |
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| 0.9804 | 1300 | 0.1212 | - | |
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| 1.0181 | 1350 | 0.0872 | - | |
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| 1.0558 | 1400 | 0.1068 | - | |
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| 1.0935 | 1450 | 0.0975 | - | |
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| 1.1312 | 1500 | 0.096 | - | |
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| 1.1689 | 1550 | 0.0649 | - | |
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| 1.2066 | 1600 | 0.1004 | - | |
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| 1.2443 | 1650 | 0.0818 | - | |
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| 1.2821 | 1700 | 0.0714 | - | |
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| 1.3198 | 1750 | 0.0875 | - | |
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| 1.3575 | 1800 | 0.0893 | - | |
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| 1.3952 | 1850 | 0.1132 | - | |
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| 1.4329 | 1900 | 0.1127 | - | |
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| 1.4706 | 1950 | 0.0707 | - | |
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| 1.5083 | 2000 | 0.0819 | - | |
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| 1.5460 | 2050 | 0.0954 | - | |
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| 1.5837 | 2100 | 0.0948 | - | |
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| 1.6214 | 2150 | 0.0953 | - | |
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| 1.6591 | 2200 | 0.0813 | - | |
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| 1.6968 | 2250 | 0.0974 | - | |
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| 1.7345 | 2300 | 0.0785 | - | |
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| 1.7722 | 2350 | 0.086 | - | |
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| 1.8100 | 2400 | 0.0808 | - | |
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| 1.8477 | 2450 | 0.1014 | - | |
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| 1.8854 | 2500 | 0.112 | - | |
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| 1.9231 | 2550 | 0.0765 | - | |
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| 1.9608 | 2600 | 0.0694 | - | |
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| 1.9985 | 2650 | 0.0915 | - | |
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| 2.0362 | 2700 | 0.087 | - | |
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| 2.0739 | 2750 | 0.0831 | - | |
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| 2.1116 | 2800 | 0.1223 | - | |
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| 2.1493 | 2850 | 0.0897 | - | |
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| 2.1870 | 2900 | 0.0937 | - | |
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| 2.2247 | 2950 | 0.0862 | - | |
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| 2.2624 | 3000 | 0.0977 | - | |
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| 2.3002 | 3050 | 0.0563 | - | |
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| 2.3379 | 3100 | 0.1197 | - | |
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| 2.3756 | 3150 | 0.095 | - | |
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| 2.4133 | 3200 | 0.0702 | - | |
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| 2.4510 | 3250 | 0.0823 | - | |
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| 2.4887 | 3300 | 0.1309 | - | |
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| 2.5264 | 3350 | 0.0612 | - | |
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| 2.5641 | 3400 | 0.0994 | - | |
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| 2.6018 | 3450 | 0.0904 | - | |
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| 2.6395 | 3500 | 0.0678 | - | |
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| 2.6772 | 3550 | 0.0896 | - | |
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| 2.7149 | 3600 | 0.0753 | - | |
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| 2.7526 | 3650 | 0.0997 | - | |
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| 2.7903 | 3700 | 0.0956 | - | |
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| 2.8281 | 3750 | 0.1016 | - | |
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| 2.8658 | 3800 | 0.0784 | - | |
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| 2.9035 | 3850 | 0.0911 | - | |
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| 2.9412 | 3900 | 0.0485 | - | |
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| 2.9789 | 3950 | 0.1078 | - | |
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| 3.0166 | 4000 | 0.0659 | - | |
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| 3.0543 | 4050 | 0.0802 | - | |
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| 3.0920 | 4100 | 0.12 | - | |
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| 3.1297 | 4150 | 0.0519 | - | |
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| 3.1674 | 4200 | 0.047 | - | |
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| 3.2051 | 4250 | 0.0906 | - | |
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| 3.2428 | 4300 | 0.0999 | - | |
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| 3.2805 | 4350 | 0.059 | - | |
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| 3.3183 | 4400 | 0.0533 | - | |
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| 3.3560 | 4450 | 0.1033 | - | |
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| 3.3937 | 4500 | 0.0871 | - | |
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| 3.4314 | 4550 | 0.065 | - | |
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| 3.4691 | 4600 | 0.1487 | - | |
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| 3.5068 | 4650 | 0.0542 | - | |
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| 3.5445 | 4700 | 0.0846 | - | |
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| 3.5822 | 4750 | 0.0756 | - | |
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| 3.6199 | 4800 | 0.0518 | - | |
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| 3.6576 | 4850 | 0.1035 | - | |
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| 3.6953 | 4900 | 0.1129 | - | |
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| 3.7330 | 4950 | 0.1319 | - | |
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| 3.7707 | 5000 | 0.0804 | - | |
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| 3.8084 | 5050 | 0.108 | - | |
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| 3.8462 | 5100 | 0.1246 | - | |
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| 3.8839 | 5150 | 0.0923 | - | |
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| 3.9216 | 5200 | 0.1048 | - | |
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| 3.9593 | 5250 | 0.0951 | - | |
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| 3.9970 | 5300 | 0.1015 | - | |
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| 4.0347 | 5350 | 0.0888 | - | |
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| 4.0724 | 5400 | 0.0917 | - | |
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| 4.1101 | 5450 | 0.0823 | - | |
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| 4.1478 | 5500 | 0.0882 | - | |
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| 4.1855 | 5550 | 0.0807 | - | |
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| 4.2232 | 5600 | 0.0997 | - | |
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| 4.2609 | 5650 | 0.0782 | - | |
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| 4.2986 | 5700 | 0.1165 | - | |
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| 4.3363 | 5750 | 0.0837 | - | |
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| 4.3741 | 5800 | 0.1098 | - | |
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| 4.4118 | 5850 | 0.0564 | - | |
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| 4.4495 | 5900 | 0.0715 | - | |
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| 4.4872 | 5950 | 0.0858 | - | |
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| 4.5249 | 6000 | 0.0889 | - | |
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| 4.5626 | 6050 | 0.0719 | - | |
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| 4.6003 | 6100 | 0.1076 | - | |
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| 4.6380 | 6150 | 0.1044 | - | |
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| 4.6757 | 6200 | 0.0914 | - | |
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| 4.7134 | 6250 | 0.1078 | - | |
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| 4.7511 | 6300 | 0.1137 | - | |
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| 4.7888 | 6350 | 0.0666 | - | |
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| 4.8265 | 6400 | 0.1009 | - | |
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| 4.8643 | 6450 | 0.0537 | - | |
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| 4.9020 | 6500 | 0.0576 | - | |
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| 4.9397 | 6550 | 0.1366 | - | |
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| 4.9774 | 6600 | 0.1009 | - | |
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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: 2.3.1 |
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- Transformers: 4.35.2 |
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- PyTorch: 2.1.0+cu121 |
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- Datasets: 2.17.0 |
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- Tokenizers: 0.15.1 |
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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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