--- library_name: setfit tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer metrics: - accuracy widget: - text: According to the second chart the most popular country visited by UK residents at this period of time was France, which was visited by about 11 millions of people of people. - text: According to first diagramm, half of Yemen's population in 2000 was children 0-14 years old. - text: After 1980 part old people in USA rose slight and in Sweden this point stay unchanged. - text: According to this charts people from the group 0-14 years take the biggest proportion from Yemen citizens in 2001. - text: 'After 1996 the numbers in the USA and Sweden began to differ: while in the USA the number of aged people fluctuated at the point of 14,8%, the population of Sweden outlived a considerable growth from 13% to 20% in 2010.' pipeline_tag: text-classification inference: true base_model: sentence-transformers/all-MiniLM-L6-v2 model-index: - name: SetFit with sentence-transformers/all-MiniLM-L6-v2 results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: accuracy value: 0.6197183098591549 name: Accuracy --- # SetFit with sentence-transformers/all-MiniLM-L6-v2 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-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. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - **Model Type:** SetFit - **Sentence Transformer body:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - **Maximum Sequence Length:** 256 tokens - **Number of Classes:** 5 classes ### Model Sources - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels | Label | Examples | |:-----------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Word form transmission | | | Tense semantics | | | Synonyms | | | Copying expression | | | Transliteration | | ## Evaluation ### Metrics | Label | Accuracy | |:--------|:---------| | **all** | 0.6197 | ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("Zlovoblachko/L1-classifier") # Run inference preds = model("After 1980 part old people in USA rose slight and in Sweden this point stay unchanged.") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:-------|:----| | Word count | 2 | 21.005 | 47 | | Label | Training Sample Count | |:-----------------------|:----------------------| | Synonyms | 99 | | Copying expression | 26 | | Tense semantics | 27 | | Word form transmission | 40 | | Transliteration | 8 | ### Training Hyperparameters - batch_size: (32, 32) - num_epochs: (10, 10) - max_steps: -1 - sampling_strategy: oversampling - body_learning_rate: (2e-05, 1e-05) - head_learning_rate: 0.01 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:------:|:----:|:-------------:|:---------------:| | 0.0012 | 1 | 0.3375 | - | | 0.0590 | 50 | 0.3628 | - | | 0.1179 | 100 | 0.3312 | - | | 0.1769 | 150 | 0.2342 | - | | 0.2358 | 200 | 0.2665 | - | | 0.2948 | 250 | 0.1857 | - | | 0.3538 | 300 | 0.2134 | - | | 0.4127 | 350 | 0.1786 | - | | 0.4717 | 400 | 0.092 | - | | 0.5307 | 450 | 0.2031 | - | | 0.5896 | 500 | 0.1449 | - | | 0.6486 | 550 | 0.1234 | - | | 0.7075 | 600 | 0.0552 | - | | 0.7665 | 650 | 0.0693 | - | | 0.8255 | 700 | 0.097 | - | | 0.8844 | 750 | 0.0448 | - | | 0.9434 | 800 | 0.041 | - | | 1.0024 | 850 | 0.0431 | - | | 1.0613 | 900 | 0.0227 | - | | 1.1203 | 950 | 0.061 | - | | 1.1792 | 1000 | 0.0209 | - | | 1.2382 | 1050 | 0.0071 | - | | 1.2972 | 1100 | 0.0285 | - | | 1.3561 | 1150 | 0.0039 | - | | 1.4151 | 1200 | 0.0029 | - | | 1.4741 | 1250 | 0.0097 | - | | 1.5330 | 1300 | 0.0076 | - | | 1.5920 | 1350 | 0.0021 | - | | 1.6509 | 1400 | 0.015 | - | | 1.7099 | 1450 | 0.0027 | - | | 1.7689 | 1500 | 0.0204 | - | | 1.8278 | 1550 | 0.013 | - | | 1.8868 | 1600 | 0.0222 | - | | 1.9458 | 1650 | 0.0427 | - | | 2.0047 | 1700 | 0.0181 | - | | 2.0637 | 1750 | 0.0232 | - | | 2.1226 | 1800 | 0.0053 | - | | 2.1816 | 1850 | 0.0169 | - | | 2.2406 | 1900 | 0.006 | - | | 2.2995 | 1950 | 0.0108 | - | | 2.3585 | 2000 | 0.0034 | - | | 2.4175 | 2050 | 0.0198 | - | | 2.4764 | 2100 | 0.0006 | - | | 2.5354 | 2150 | 0.0142 | - | | 2.5943 | 2200 | 0.0038 | - | | 2.6533 | 2250 | 0.0006 | - | | 2.7123 | 2300 | 0.0007 | - | | 2.7712 | 2350 | 0.0012 | - | | 2.8302 | 2400 | 0.0003 | - | | 2.8892 | 2450 | 0.0127 | - | | 2.9481 | 2500 | 0.0181 | - | | 3.0071 | 2550 | 0.006 | - | | 3.0660 | 2600 | 0.0006 | - | | 3.125 | 2650 | 0.0156 | - | | 3.1840 | 2700 | 0.0427 | - | | 3.2429 | 2750 | 0.0004 | - | | 3.3019 | 2800 | 0.0013 | - | | 3.3608 | 2850 | 0.0241 | - | | 3.4198 | 2900 | 0.0004 | - | | 3.4788 | 2950 | 0.0048 | - | | 3.5377 | 3000 | 0.0004 | - | | 3.5967 | 3050 | 0.0006 | - | | 3.6557 | 3100 | 0.0044 | - | | 3.7146 | 3150 | 0.0142 | - | | 3.7736 | 3200 | 0.005 | - | | 3.8325 | 3250 | 0.0022 | - | | 3.8915 | 3300 | 0.0033 | - | | 3.9505 | 3350 | 0.0033 | - | | 4.0094 | 3400 | 0.0005 | - | | 4.0684 | 3450 | 0.0299 | - | | 4.1274 | 3500 | 0.0172 | - | | 4.1863 | 3550 | 0.0079 | - | | 4.2453 | 3600 | 0.0012 | - | | 4.3042 | 3650 | 0.0093 | - | | 4.3632 | 3700 | 0.0175 | - | | 4.4222 | 3750 | 0.0278 | - | | 4.4811 | 3800 | 0.0004 | - | | 4.5401 | 3850 | 0.0054 | - | | 4.5991 | 3900 | 0.002 | - | | 4.6580 | 3950 | 0.0248 | - | | 4.7170 | 4000 | 0.0173 | - | | 4.7759 | 4050 | 0.0004 | - | | 4.8349 | 4100 | 0.0154 | - | | 4.8939 | 4150 | 0.0162 | - | | 4.9528 | 4200 | 0.0052 | - | | 5.0118 | 4250 | 0.0142 | - | | 5.0708 | 4300 | 0.0109 | - | | 5.1297 | 4350 | 0.0003 | - | | 5.1887 | 4400 | 0.0002 | - | | 5.2476 | 4450 | 0.0003 | - | | 5.3066 | 4500 | 0.0081 | - | | 5.3656 | 4550 | 0.0005 | - | | 5.4245 | 4600 | 0.0229 | - | | 5.4835 | 4650 | 0.0002 | - | | 5.5425 | 4700 | 0.0004 | - | | 5.6014 | 4750 | 0.0233 | - | | 5.6604 | 4800 | 0.0086 | - | | 5.7193 | 4850 | 0.0084 | - | | 5.7783 | 4900 | 0.0177 | - | | 5.8373 | 4950 | 0.0102 | - | | 5.8962 | 5000 | 0.017 | - | | 5.9552 | 5050 | 0.0037 | - | | 6.0142 | 5100 | 0.005 | - | | 6.0731 | 5150 | 0.0002 | - | | 6.1321 | 5200 | 0.0188 | - | | 6.1910 | 5250 | 0.0037 | - | | 6.25 | 5300 | 0.0003 | - | | 6.3090 | 5350 | 0.0137 | - | | 6.3679 | 5400 | 0.0107 | - | | 6.4269 | 5450 | 0.0045 | - | | 6.4858 | 5500 | 0.0002 | - | | 6.5448 | 5550 | 0.0238 | - | | 6.6038 | 5600 | 0.0209 | - | | 6.6627 | 5650 | 0.0003 | - | | 6.7217 | 5700 | 0.0002 | - | | 6.7807 | 5750 | 0.0029 | - | | 6.8396 | 5800 | 0.0177 | - | | 6.8986 | 5850 | 0.0165 | - | | 6.9575 | 5900 | 0.0045 | - | | 7.0165 | 5950 | 0.0203 | - | | 7.0755 | 6000 | 0.0048 | - | | 7.1344 | 6050 | 0.0251 | - | | 7.1934 | 6100 | 0.0147 | - | | 7.2524 | 6150 | 0.0033 | - | | 7.3113 | 6200 | 0.0166 | - | | 7.3703 | 6250 | 0.0129 | - | | 7.4292 | 6300 | 0.0169 | - | | 7.4882 | 6350 | 0.0001 | - | | 7.5472 | 6400 | 0.0002 | - | | 7.6061 | 6450 | 0.0029 | - | | 7.6651 | 6500 | 0.0264 | - | | 7.7241 | 6550 | 0.0079 | - | | 7.7830 | 6600 | 0.0002 | - | | 7.8420 | 6650 | 0.0157 | - | | 7.9009 | 6700 | 0.0116 | - | | 7.9599 | 6750 | 0.0031 | - | | 8.0189 | 6800 | 0.0055 | - | | 8.0778 | 6850 | 0.0113 | - | | 8.1368 | 6900 | 0.0004 | - | | 8.1958 | 6950 | 0.0301 | - | | 8.2547 | 7000 | 0.0002 | - | | 8.3137 | 7050 | 0.0169 | - | | 8.3726 | 7100 | 0.0001 | - | | 8.4316 | 7150 | 0.0165 | - | | 8.4906 | 7200 | 0.0201 | - | | 8.5495 | 7250 | 0.0168 | - | | 8.6085 | 7300 | 0.0197 | - | | 8.6675 | 7350 | 0.0165 | - | | 8.7264 | 7400 | 0.0165 | - | | 8.7854 | 7450 | 0.0002 | - | | 8.8443 | 7500 | 0.0134 | - | | 8.9033 | 7550 | 0.0037 | - | | 8.9623 | 7600 | 0.0043 | - | | 9.0212 | 7650 | 0.0001 | - | | 9.0802 | 7700 | 0.0034 | - | | 9.1392 | 7750 | 0.0036 | - | | 9.1981 | 7800 | 0.0001 | - | | 9.2571 | 7850 | 0.0069 | - | | 9.3160 | 7900 | 0.0304 | - | | 9.375 | 7950 | 0.0203 | - | | 9.4340 | 8000 | 0.0002 | - | | 9.4929 | 8050 | 0.0002 | - | | 9.5519 | 8100 | 0.0058 | - | | 9.6108 | 8150 | 0.0141 | - | | 9.6698 | 8200 | 0.0031 | - | | 9.7288 | 8250 | 0.0169 | - | | 9.7877 | 8300 | 0.0002 | - | | 9.8467 | 8350 | 0.0075 | - | | 9.9057 | 8400 | 0.0192 | - | | 9.9646 | 8450 | 0.0588 | - | ### Framework Versions - Python: 3.10.12 - SetFit: 1.1.0.dev0 - Sentence Transformers: 2.6.1 - Transformers: 4.38.2 - PyTorch: 2.2.1+cu121 - Datasets: 2.18.0 - Tokenizers: 0.15.2 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```