| | --- |
| | library_name: setfit |
| | tags: |
| | - setfit |
| | - sentence-transformers |
| | - text-classification |
| | - generated_from_setfit_trainer |
| | metrics: |
| | - accuracy |
| | widget: |
| | - text: 'The Vitorian team knew to make up for the significant absences of Herrmann |
| | , Oleson , Huertas and Micov with a big dose of involvement and teamwork , even |
| | though it had to hold out until the end to take the victory . ' |
| | - text: '`` But why pay her bills ? ' |
| | - text: 'In the body , pemetrexed is converted into an active form that blocks the |
| | activity of the enzymes that are involved in producing nucleotides ( the building |
| | blocks of DNA and RNA , the genetic material of cells ) . ' |
| | - text: '`` The daily crush of media tweets , cameras and reporters outside the courthouse |
| | , '''' the lawyers wrote , `` was unlike anything ever seen here in New Haven |
| | and maybe statewide . '''' ' |
| | - text: 'However , in both studies , patients whose cancer was not affecting squamous |
| | cells had longer survival times if they received Alimta than if they received |
| | the comparator . ' |
| | pipeline_tag: text-classification |
| | inference: true |
| | base_model: sentence-transformers/paraphrase-mpnet-base-v2 |
| | --- |
| | |
| | # SetFit with sentence-transformers/paraphrase-mpnet-base-v2 |
| |
|
| | 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. |
| |
|
| | 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/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) |
| | - **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance |
| | - **Maximum Sequence Length:** 512 tokens |
| | - **Number of Classes:** 7 classes |
| | <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> |
| | <!-- - **Language:** Unknown --> |
| | <!-- - **License:** Unknown --> |
| |
|
| | ### 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 | |
| | |:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | | 6 | <ul><li>'If you were especially helpful in a corrupt scheme you received not just cash in a bag , but equity . '</li><li>'Two American companies reached deals for fields auctioned in June . '</li><li>'Let me prove it , Phil . '</li></ul> | |
| | | 2 | <ul><li>'This building shook like hell and it kept getting stronger . '</li><li>'Now you could ask me , why should the user mind about MathML ? '</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></ul> | |
| | | 3 | <ul><li>'The tumor , he suggested , developed when the second , normal copy also was damaged . '</li><li>'Proper English bells are started off in `` rounds , `` from the highest-pitched bell to the lowest -- a simple descending scale using , in larger churches , as many as 12 bells . '</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> | |
| | | 5 | <ul><li>'Schools that are structured in this way produce students with higher morale and superior academic performance . '</li><li>'I got home , let the dogs into the house and noticed some sounds above my head , as if someone were walking on the roof , or upstairs . '</li><li>'Give me your address . '</li></ul> | |
| | | 0 | <ul><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><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></ul> | |
| | | 4 | <ul><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><li>'Cerenia contains the active substance maropitant and is available as tablet or as solution for injection . '</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></ul> | |
| | | 1 | <ul><li>'`` The sound of bells is a net to draw people into the church , `` he says . '</li><li>'`` Progressive education `` ( as it was once called ) is far more interesting and agreeable to teachers than is disciplined instruction . '</li><li>"The defense lawyers also claim , for example , that Mr. Hayes may have been prejudiced when Judge Blue declined to allow them to test potential jurors ' reactions by showing them grisly crime-scene photographs during jury selection . "</li></ul> | |
| |
|
| | ## 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("HelgeKn/SemEval-multi-class-v1-10") |
| | # Run inference |
| | preds = model("`` But why pay her bills ? ") |
| | ``` |
| |
|
| | <!-- |
| | ### Downstream Use |
| |
|
| | *List how someone could finetune this model on their own dataset.* |
| | --> |
| |
|
| | <!-- |
| | ### Out-of-Scope Use |
| |
|
| | *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| | --> |
| |
|
| | <!-- |
| | ## Bias, Risks and Limitations |
| |
|
| | *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| | --> |
| |
|
| | <!-- |
| | ### Recommendations |
| |
|
| | *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| | --> |
| |
|
| | ## Training Details |
| |
|
| | ### Training Set Metrics |
| | | Training set | Min | Median | Max | |
| | |:-------------|:----|:--------|:----| |
| | | Word count | 5 | 25.8286 | 75 | |
| |
|
| | | Label | Training Sample Count | |
| | |:------|:----------------------| |
| | | 0 | 10 | |
| | | 1 | 10 | |
| | | 2 | 10 | |
| | | 3 | 10 | |
| | | 4 | 10 | |
| | | 5 | 10 | |
| | | 6 | 10 | |
| |
|
| | ### Training Hyperparameters |
| | - batch_size: (16, 16) |
| | - num_epochs: (4, 4) |
| | - max_steps: -1 |
| | - sampling_strategy: oversampling |
| | - num_iterations: 20 |
| | - body_learning_rate: (2e-05, 2e-05) |
| | - head_learning_rate: 2e-05 |
| | - 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 | |
| | |:------:|:----:|:-------------:|:---------------:| |
| | | 0.0057 | 1 | 0.2314 | - | |
| | | 0.2857 | 50 | 0.218 | - | |
| | | 0.5714 | 100 | 0.1161 | - | |
| | | 0.8571 | 150 | 0.0559 | - | |
| | | 1.1429 | 200 | 0.0087 | - | |
| | | 1.4286 | 250 | 0.0029 | - | |
| | | 1.7143 | 300 | 0.001 | - | |
| | | 2.0 | 350 | 0.0006 | - | |
| | | 2.2857 | 400 | 0.0011 | - | |
| | | 2.5714 | 450 | 0.0009 | - | |
| | | 2.8571 | 500 | 0.0005 | - | |
| | | 3.1429 | 550 | 0.0006 | - | |
| | | 3.4286 | 600 | 0.0004 | - | |
| | | 3.7143 | 650 | 0.0003 | - | |
| | | 4.0 | 700 | 0.0005 | - | |
| | |
| | ### Framework Versions |
| | - Python: 3.9.13 |
| | - SetFit: 1.0.1 |
| | - Sentence Transformers: 2.2.2 |
| | - Transformers: 4.36.0 |
| | - PyTorch: 2.1.1+cpu |
| | - Datasets: 2.15.0 |
| | - Tokenizers: 0.15.0 |
| | |
| | ## 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} |
| | } |
| | ``` |
| | |
| | <!-- |
| | ## Glossary |
| | |
| | *Clearly define terms in order to be accessible across audiences.* |
| | --> |
| | |
| | <!-- |
| | ## Model Card Authors |
| | |
| | *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
| | --> |
| | |
| | <!-- |
| | ## Model Card Contact |
| | |
| | *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
| | --> |
