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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: 'The Vitorian team knew to make up for the significant absences of Herrmann |
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, Oleson , Huertas and Micov with a big dose of involvement and teamwork , even |
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though it had to hold out until the end to take the victory . ' |
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- text: '`` But why pay her bills ? ' |
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- text: 'In the body , pemetrexed is converted into an active form that blocks the |
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activity of the enzymes that are involved in producing nucleotides ( the building |
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blocks of DNA and RNA , the genetic material of cells ) . ' |
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- text: '`` The daily crush of media tweets , cameras and reporters outside the courthouse |
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, '''' the lawyers wrote , `` was unlike anything ever seen here in New Haven |
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and maybe statewide . '''' ' |
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- text: 'However , in both studies , patients whose cancer was not affecting squamous |
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cells had longer survival times if they received Alimta than if they received |
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the comparator . ' |
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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: 0.16158940397350993 |
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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:** 7 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>'There were relatively few cases reported of attempts to involve users in service planning but their involvement in service provision was found to be more common . '</li><li>"At St. Mary 's Church in Ilminster , Somerset , the bells have fallen silent following a dust-up over church attendance . "</li><li>'Treatment should be delayed or discontinued , or the dose reduced , in patients whose blood counts are abnormal or who have certain other side effects . '</li></ul> | |
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| 6 | <ul><li>'If you were especially helpful in a corrupt scheme you received not just cash in a bag , but equity . '</li><li>"Moreover , conservatives argue that it 's Justice Elena Kagan who has an ethical issue , not Scalia and Thomas . "</li><li>'No one speaks , and the snaking of the ropes seems to make as much sound as the bells themselves , muffled by the ceiling . '</li></ul> | |
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| 2 | <ul><li>'In and around all levels of government in the U.S. are groups of people who can best be described as belonging to a political insider commercial party . '</li><li>'The report and a casebook of initiatives will be published in 1996 and provide the backdrop for a conference to be staged in Autumn , 1996 . '</li><li>'This building shook like hell and it kept getting stronger . '</li></ul> | |
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| 0 | <ul><li>'For months the Johns Hopkins researchers , using gene probes , experimentally crawled down the length of chromosome 17 , looking for the smallest common bit of genetic material lost in all tumor cells . '</li><li>'It explains how the Committee for Medicinal Products for Human Use ( CHMP ) assessed the studies performed , to reach their recommendations on how to use the medicine . '</li><li>'-- Most important of all , schools should have principals with a large measure of authority over the faculty , the curriculum , and all matters of student discipline . '</li></ul> | |
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| 5 | <ul><li>': = : It is used to define a variable value . '</li><li>'I could also see the clouds across the bay from the horrible fire in the Marina District of San Francisco . '</li><li>'The man with the clipboard represented a halfhearted attempt to introduce a bit of les sportif into our itinerary . '</li></ul> | |
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| 4 | <ul><li>"First , why ticket splitting has increased and taken the peculiar pattern that it has over the past half century : Prior to the election of Franklin Roosevelt as president and the advent of the New Deal , government occupied a much smaller role in society and the prisoner 's dilemma problem confronting voters in races for Congress was considerably less severe . "</li><li>'The second quarter was more of the same , but the Alavan team opted for the inside game of Barac and the work of Eliyahu , who was greeted with whistles and applause at his return home , to continue increasing their lead by half-time ( 34-43 ) . '</li><li>'In 2005 , the fear of invasion of the national territory by hordes of Polish plumbers was felt both on the Left and on the Right . '</li></ul> | |
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| 1 | <ul><li>'`` Progressive education `` ( as it was once called ) is far more interesting and agreeable to teachers than is disciplined instruction . '</li><li>"Ringing does become a bit of an obsession , `` admits Stephanie Pattenden , master of the band at St. Mary Abbot and one of England 's best female ringers . "</li><li>"He says the neighbors complain , but I do n't believe it . "</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** | 0.1616 | |
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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/SemEval-multi-label-v2") |
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# Run inference |
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preds = model("`` But why pay her bills ? ") |
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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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## 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 | 6 | 25.8929 | 75 | |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| 0 | 8 | |
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| 1 | 8 | |
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| 2 | 8 | |
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| 3 | 8 | |
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| 4 | 8 | |
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| 5 | 8 | |
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| 6 | 8 | |
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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: 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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| 0.0071 | 1 | 0.2758 | - | |
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| 0.3571 | 50 | 0.1622 | - | |
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| 0.7143 | 100 | 0.0874 | - | |
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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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