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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: According to the second chart the most popular country visited by UK residents |
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at this period of time was France, which was visited by about 11 millions of people |
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of people. |
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- text: According to first diagramm, half of Yemen's population in 2000 was children |
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0-14 years old. |
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- text: After 1980 part old people in USA rose slight and in Sweden this point stay |
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unchanged. |
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- text: According to this charts people from the group 0-14 years take the biggest |
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proportion from Yemen citizens in 2001. |
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- text: 'After 1996 the numbers in the USA and Sweden began to differ: while in the |
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USA the number of aged people fluctuated at the point of 14,8%, the population |
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of Sweden outlived a considerable growth from 13% to 20% in 2010.' |
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pipeline_tag: text-classification |
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inference: true |
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base_model: sentence-transformers/all-MiniLM-L6-v2 |
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model-index: |
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- name: SetFit with sentence-transformers/all-MiniLM-L6-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: 0.6197183098591549 |
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name: Accuracy |
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--- |
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# SetFit with sentence-transformers/all-MiniLM-L6-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/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. |
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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/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-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:** 256 tokens |
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- **Number of Classes:** 5 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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| Word form transmission | <ul><li>"Mother should take care of her own child at first, by this quote we simply can see that problems of government's own country should be placed on the first position."</li><li>"A building's style may say a lot about its history."</li><li>'A lot of artists and entertainment organisations have financional costs because of free using of their contents in the Internet.'</li></ul> | |
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| Tense semantics | <ul><li>'Samsung, "Blackberry" and "HTC" in 2015 have almost the same percentage share.'</li><li>'(5,9%) Overall, almost all unemployment rates have remained on the same level between 2014 and 2015, except EU, Latin America and Middle East.'</li><li>'15% consist of things which are transported by rail in Eastern Europe in 2008.'</li></ul> | |
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| Synonyms | <ul><li>'(the destination between Moscow and Saint Petersburg, for instance, can be easily overcame by "Lastochka" train for 5 hours).'</li><li>'(the destination between Moscow and Saint Petersburg, for instance, can be easily overcame by "Lastochka" train for 5 hours).'</li><li>'There is an extremely clear difference: there are too many men on a tech subjects.'</li></ul> | |
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| Copying expression | <ul><li>'15-59 years people in Yemen are increasing, while in Italy this number decreases.'</li><li>'2013 year is a key one.'</li><li>'3,6% are people have age 60+ years.'</li></ul> | |
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| Transliteration | <ul><li>'A closer look at graphic revails that goods transported by rail had good products, which massive 11%.'</li><li>"According to first diagramm, half of Yemen's population in 2000 was children 0-14 years old."</li><li>'According to my opinion different fabrics make much more harm for our nature.'</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** | 0.6197 | |
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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("Zlovoblachko/L1-classifier") |
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# Run inference |
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preds = model("After 1980 part old people in USA rose slight and in Sweden this point stay unchanged.") |
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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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<!-- |
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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 | 2 | 21.005 | 47 | |
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| Label | Training Sample Count | |
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|:-----------------------|:----------------------| |
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| Synonyms | 99 | |
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| Copying expression | 26 | |
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| Tense semantics | 27 | |
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| Word form transmission | 40 | |
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| Transliteration | 8 | |
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### Training Hyperparameters |
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- batch_size: (32, 32) |
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- num_epochs: (10, 10) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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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: 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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| 0.0012 | 1 | 0.3375 | - | |
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| 0.0590 | 50 | 0.3628 | - | |
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| 0.1179 | 100 | 0.3312 | - | |
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| 0.1769 | 150 | 0.2342 | - | |
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| 0.2358 | 200 | 0.2665 | - | |
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| 0.2948 | 250 | 0.1857 | - | |
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| 0.3538 | 300 | 0.2134 | - | |
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| 0.4127 | 350 | 0.1786 | - | |
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| 0.4717 | 400 | 0.092 | - | |
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| 0.5307 | 450 | 0.2031 | - | |
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| 0.5896 | 500 | 0.1449 | - | |
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| 0.6486 | 550 | 0.1234 | - | |
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| 0.7075 | 600 | 0.0552 | - | |
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| 0.7665 | 650 | 0.0693 | - | |
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| 0.8255 | 700 | 0.097 | - | |
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| 0.8844 | 750 | 0.0448 | - | |
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| 0.9434 | 800 | 0.041 | - | |
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| 1.0024 | 850 | 0.0431 | - | |
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| 1.0613 | 900 | 0.0227 | - | |
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| 1.1203 | 950 | 0.061 | - | |
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| 1.1792 | 1000 | 0.0209 | - | |
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| 1.2382 | 1050 | 0.0071 | - | |
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| 1.2972 | 1100 | 0.0285 | - | |
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| 1.3561 | 1150 | 0.0039 | - | |
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| 1.4151 | 1200 | 0.0029 | - | |
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| 1.4741 | 1250 | 0.0097 | - | |
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| 1.5330 | 1300 | 0.0076 | - | |
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| 1.5920 | 1350 | 0.0021 | - | |
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| 1.6509 | 1400 | 0.015 | - | |
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| 1.7099 | 1450 | 0.0027 | - | |
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| 1.7689 | 1500 | 0.0204 | - | |
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| 1.8278 | 1550 | 0.013 | - | |
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| 1.8868 | 1600 | 0.0222 | - | |
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| 1.9458 | 1650 | 0.0427 | - | |
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| 2.0047 | 1700 | 0.0181 | - | |
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| 2.0637 | 1750 | 0.0232 | - | |
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| 2.1226 | 1800 | 0.0053 | - | |
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| 2.1816 | 1850 | 0.0169 | - | |
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| 2.2406 | 1900 | 0.006 | - | |
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| 2.2995 | 1950 | 0.0108 | - | |
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| 2.3585 | 2000 | 0.0034 | - | |
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| 2.4175 | 2050 | 0.0198 | - | |
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| 2.4764 | 2100 | 0.0006 | - | |
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| 2.5354 | 2150 | 0.0142 | - | |
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| 2.5943 | 2200 | 0.0038 | - | |
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| 2.6533 | 2250 | 0.0006 | - | |
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| 2.7123 | 2300 | 0.0007 | - | |
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| 2.7712 | 2350 | 0.0012 | - | |
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| 2.8302 | 2400 | 0.0003 | - | |
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| 2.8892 | 2450 | 0.0127 | - | |
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| 2.9481 | 2500 | 0.0181 | - | |
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| 3.0071 | 2550 | 0.006 | - | |
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| 3.0660 | 2600 | 0.0006 | - | |
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| 3.125 | 2650 | 0.0156 | - | |
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| 3.1840 | 2700 | 0.0427 | - | |
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| 3.2429 | 2750 | 0.0004 | - | |
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| 3.3019 | 2800 | 0.0013 | - | |
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| 3.3608 | 2850 | 0.0241 | - | |
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| 3.4198 | 2900 | 0.0004 | - | |
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| 3.4788 | 2950 | 0.0048 | - | |
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| 3.5377 | 3000 | 0.0004 | - | |
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| 3.5967 | 3050 | 0.0006 | - | |
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| 3.6557 | 3100 | 0.0044 | - | |
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| 3.7146 | 3150 | 0.0142 | - | |
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| 3.7736 | 3200 | 0.005 | - | |
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| 3.8325 | 3250 | 0.0022 | - | |
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| 3.8915 | 3300 | 0.0033 | - | |
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| 3.9505 | 3350 | 0.0033 | - | |
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| 4.0094 | 3400 | 0.0005 | - | |
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| 4.0684 | 3450 | 0.0299 | - | |
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| 4.1274 | 3500 | 0.0172 | - | |
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| 4.1863 | 3550 | 0.0079 | - | |
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| 4.2453 | 3600 | 0.0012 | - | |
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| 4.3042 | 3650 | 0.0093 | - | |
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| 4.3632 | 3700 | 0.0175 | - | |
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| 4.4222 | 3750 | 0.0278 | - | |
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| 4.4811 | 3800 | 0.0004 | - | |
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| 4.5401 | 3850 | 0.0054 | - | |
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| 4.5991 | 3900 | 0.002 | - | |
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| 4.6580 | 3950 | 0.0248 | - | |
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| 4.7170 | 4000 | 0.0173 | - | |
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| 4.7759 | 4050 | 0.0004 | - | |
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| 4.8349 | 4100 | 0.0154 | - | |
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| 4.8939 | 4150 | 0.0162 | - | |
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| 4.9528 | 4200 | 0.0052 | - | |
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| 5.0118 | 4250 | 0.0142 | - | |
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| 5.0708 | 4300 | 0.0109 | - | |
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| 5.1297 | 4350 | 0.0003 | - | |
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| 5.1887 | 4400 | 0.0002 | - | |
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| 5.2476 | 4450 | 0.0003 | - | |
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| 5.3066 | 4500 | 0.0081 | - | |
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| 5.3656 | 4550 | 0.0005 | - | |
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| 5.4245 | 4600 | 0.0229 | - | |
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| 5.4835 | 4650 | 0.0002 | - | |
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| 5.5425 | 4700 | 0.0004 | - | |
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| 5.6014 | 4750 | 0.0233 | - | |
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| 5.6604 | 4800 | 0.0086 | - | |
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| 5.7193 | 4850 | 0.0084 | - | |
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| 5.7783 | 4900 | 0.0177 | - | |
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| 5.8373 | 4950 | 0.0102 | - | |
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| 5.8962 | 5000 | 0.017 | - | |
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| 5.9552 | 5050 | 0.0037 | - | |
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| 6.0142 | 5100 | 0.005 | - | |
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| 6.0731 | 5150 | 0.0002 | - | |
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| 6.1321 | 5200 | 0.0188 | - | |
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| 6.1910 | 5250 | 0.0037 | - | |
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| 6.25 | 5300 | 0.0003 | - | |
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| 6.3090 | 5350 | 0.0137 | - | |
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| 6.3679 | 5400 | 0.0107 | - | |
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| 6.4269 | 5450 | 0.0045 | - | |
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| 6.4858 | 5500 | 0.0002 | - | |
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| 6.5448 | 5550 | 0.0238 | - | |
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| 6.6038 | 5600 | 0.0209 | - | |
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| 6.6627 | 5650 | 0.0003 | - | |
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| 6.7217 | 5700 | 0.0002 | - | |
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| 6.7807 | 5750 | 0.0029 | - | |
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| 6.8396 | 5800 | 0.0177 | - | |
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| 6.8986 | 5850 | 0.0165 | - | |
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| 6.9575 | 5900 | 0.0045 | - | |
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| 7.0165 | 5950 | 0.0203 | - | |
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| 7.0755 | 6000 | 0.0048 | - | |
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| 7.1344 | 6050 | 0.0251 | - | |
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| 7.1934 | 6100 | 0.0147 | - | |
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| 7.2524 | 6150 | 0.0033 | - | |
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| 7.3113 | 6200 | 0.0166 | - | |
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| 7.3703 | 6250 | 0.0129 | - | |
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| 7.4292 | 6300 | 0.0169 | - | |
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| 7.4882 | 6350 | 0.0001 | - | |
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| 7.5472 | 6400 | 0.0002 | - | |
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| 7.6061 | 6450 | 0.0029 | - | |
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| 7.6651 | 6500 | 0.0264 | - | |
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| 7.7241 | 6550 | 0.0079 | - | |
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| 7.7830 | 6600 | 0.0002 | - | |
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| 7.8420 | 6650 | 0.0157 | - | |
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| 7.9009 | 6700 | 0.0116 | - | |
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| 7.9599 | 6750 | 0.0031 | - | |
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| 8.0189 | 6800 | 0.0055 | - | |
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| 8.0778 | 6850 | 0.0113 | - | |
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| 8.1368 | 6900 | 0.0004 | - | |
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| 8.1958 | 6950 | 0.0301 | - | |
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| 8.2547 | 7000 | 0.0002 | - | |
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| 8.3137 | 7050 | 0.0169 | - | |
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| 8.3726 | 7100 | 0.0001 | - | |
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| 8.4316 | 7150 | 0.0165 | - | |
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| 8.4906 | 7200 | 0.0201 | - | |
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| 8.5495 | 7250 | 0.0168 | - | |
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| 8.6085 | 7300 | 0.0197 | - | |
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| 8.6675 | 7350 | 0.0165 | - | |
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| 8.7264 | 7400 | 0.0165 | - | |
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| 8.7854 | 7450 | 0.0002 | - | |
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| 8.8443 | 7500 | 0.0134 | - | |
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| 8.9033 | 7550 | 0.0037 | - | |
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| 8.9623 | 7600 | 0.0043 | - | |
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| 9.0212 | 7650 | 0.0001 | - | |
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| 9.0802 | 7700 | 0.0034 | - | |
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| 9.1392 | 7750 | 0.0036 | - | |
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| 9.1981 | 7800 | 0.0001 | - | |
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| 9.2571 | 7850 | 0.0069 | - | |
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| 9.3160 | 7900 | 0.0304 | - | |
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| 9.375 | 7950 | 0.0203 | - | |
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| 9.4340 | 8000 | 0.0002 | - | |
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| 9.4929 | 8050 | 0.0002 | - | |
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| 9.5519 | 8100 | 0.0058 | - | |
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| 9.6108 | 8150 | 0.0141 | - | |
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| 9.6698 | 8200 | 0.0031 | - | |
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| 9.7288 | 8250 | 0.0169 | - | |
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| 9.7877 | 8300 | 0.0002 | - | |
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| 9.8467 | 8350 | 0.0075 | - | |
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| 9.9057 | 8400 | 0.0192 | - | |
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| 9.9646 | 8450 | 0.0588 | - | |
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### Framework Versions |
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- Python: 3.10.12 |
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- SetFit: 1.1.0.dev0 |
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- Sentence Transformers: 2.6.1 |
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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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