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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: Buses are more simple - you just buy a ticket . |
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- text: As citizens of village , we totally care about environment of our village |
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. |
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- text: So , finally I suggest that it would be a great idea to combine the different |
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types of activities , both popular and the newest . |
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- text: Had 12 years old . |
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- text: On the other hand , I have the theoretical knowledge to use new the technologies |
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this great project requires . |
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pipeline_tag: text-classification |
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inference: true |
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base_model: sentence-transformers/paraphrase-mpnet-base-v2 |
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model-index: |
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- name: SetFit with sentence-transformers/paraphrase-mpnet-base-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: 0.15543478260869564 |
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name: Accuracy |
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--- |
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# SetFit with sentence-transformers/paraphrase-mpnet-base-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-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) 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-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) |
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- **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance |
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- **Maximum Sequence Length:** 512 tokens |
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- **Number of Classes:** 8 classes |
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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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| 3 | <ul><li>"Usually there are generation problems , sons do n't understand parents and vicecersa , but dialoging and listening emotions and facts , everyone can have another point of view ."</li><li>'While youngsters use their time trying to get concerned the oldest people from de village about the importance of the care of our surroundings , middle - aged people planted many trees around the village and cleaned the floor of our public places making a more attractive place to live than we used to have .'</li><li>'As an example , if you are able to get alone with your travel companion could enjoy each moment of the trip , exchange some pictures , eat together , and visit places with common interest such as museums or malls .'</li></ul> | |
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| 5 | <ul><li>'Michael get away from there .'</li><li>'I guess that in our future there are no helicopters , and not even cars .'</li><li>'In addition , to decrease the risk of negative comments or posts , Facebook and Twitter would improve their futures to solve the less personal privacy problem .'</li></ul> | |
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| 4 | <ul><li>'Something that they don know was that the whole thing was a movie !'</li><li>'Yours Sincerely .'</li><li>"stop shouting . do n't shout ."</li></ul> | |
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| 2 | <ul><li>'X " admitted to a state psychiatric hospital after being found not competent to stand trial on charges of stalking harassment , trespassing and telephone harassment " ( pp .'</li><li>'It is a job with a lot of interesting aspects ,'</li><li>'On balance , learning foreign languages is very positive on different aspect , so if you have the positivity of learning a new language do it , because it will bring you many benefits .'</li></ul> | |
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| 6 | <ul><li>'In addition , she has no blithe memory in her childhood .'</li><li>'The aim of this report is to give you my personal point of view of the course I did in your branch in Madrid last month .'</li><li>'I know you are searching for a flat to live for the whole next year .'</li></ul> | |
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| 0 | <ul><li>'In China , English is took to be a foreign language which many students choose to learn .'</li><li>'No one can deny that the pollution issue is one of the utmost important thing which should be prevented .'</li><li>'The third section is to print the prepared bank notes .'</li></ul> | |
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| 1 | <ul><li>'They use at least one hour to learn English knowledge a day .'</li><li>'If you want to see that movie , you need to watch the first 3 movies before to understand it .'</li><li>'Next to go would be , students get used to relax by having no study and homework in the long vacation .'</li></ul> | |
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| 7 | <ul><li>'To start with , there are a wide range of troublesome it maybe leadding to this phenomeon .'</li><li>'Secondly , the families could give you some advice about how to deal with the things which will cause trouble .'</li><li>'I been twelve years practice volleyball and because of it I knew lot of people who help me to grow up in the sport and life .'</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** | 0.1554 | |
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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("HelgeKn/BEA2019-multi-class-10") |
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# Run inference |
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preds = model("Had 12 years old .") |
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``` |
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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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*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 | 3 | 21.3375 | 56 | |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| 0 | 10 | |
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| 1 | 10 | |
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| 2 | 10 | |
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| 3 | 10 | |
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| 4 | 10 | |
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| 5 | 10 | |
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| 6 | 10 | |
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| 7 | 10 | |
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### Training Hyperparameters |
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- batch_size: (16, 16) |
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- num_epochs: (2, 2) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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- num_iterations: 20 |
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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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| 0.005 | 1 | 0.2242 | - | |
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| 0.25 | 50 | 0.1786 | - | |
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| 0.5 | 100 | 0.1831 | - | |
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| 0.75 | 150 | 0.0221 | - | |
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| 1.0 | 200 | 0.0127 | - | |
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| 1.25 | 250 | 0.0064 | - | |
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| 1.5 | 300 | 0.0045 | - | |
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| 1.75 | 350 | 0.0028 | - | |
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| 2.0 | 400 | 0.002 | - | |
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### Framework Versions |
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- Python: 3.9.13 |
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- SetFit: 1.0.1 |
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- Sentence Transformers: 2.2.2 |
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- Transformers: 4.36.0 |
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- PyTorch: 2.1.1+cpu |
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- Datasets: 2.15.0 |
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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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