| --- |
| library_name: setfit |
| tags: |
| - setfit |
| - sentence-transformers |
| - text-classification |
| - generated_from_setfit_trainer |
| metrics: |
| - accuracy |
| widget: |
| - text: 'The Alavas worked themselves to the bone in the last period , and English |
| and San Emeterio ( 65-75 ) had already made it clear that they were not going |
| to let anyone take away what they had earned during the first thirty minutes . ' |
| - text: 'To break the uncomfortable silence , Haney began to talk . ' |
| - text: 'For the treatment of non-small cell lung cancer , the effects of Alimta were |
| compared with those of docetaxel ( another anticancer medicine ) in one study |
| involving 571 patients with locally advanced or metastatic disease who had received |
| chemotherapy in the past . ' |
| - text: 'As we all know , a few minutes before the end of the game ( that their team |
| had already won ) , both players deliberately wasted time which made the referee |
| show the second yellow card to both of them . ' |
| - text: 'In contrast , patients whose cancer was affecting squamous cells had shorter |
| survival times if they received Alimta . ' |
| pipeline_tag: text-classification |
| inference: true |
| base_model: sentence-transformers/paraphrase-mpnet-base-v2 |
| model-index: |
| - name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2 |
| results: |
| - task: |
| type: text-classification |
| name: Text Classification |
| dataset: |
| name: Unknown |
| type: unknown |
| split: test |
| metrics: |
| - type: accuracy |
| value: 0.1271523178807947 |
| name: Accuracy |
| --- |
| |
| # 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 | |
| |:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | 4 | <ul><li>'One writer , signing his letter as `` Red-blooded , balanced male , `` remarked on the `` frequency of women fainting in peals , `` and suggested that they `` settle back into their traditional role of making tea at meetings . `` '</li><li>'`` No offense intended `` , he said gently . '</li><li>"`` It 's my line of work `` , he said "</li></ul> | |
| | 3 | <ul><li>"It was the most exercise we 'd had all morning and it was followed by our driving immediately to the nearest watering hole . "</li><li>'Alimta is used together with cisplatin ( another anticancer medicine ) when the cancer is unresectable ( cannot be removed by surgery alone ) and malignant ( has spread , or is likely to spread easily , to other parts of the body ) , in patients who have not received chemotherapy ( medicines for cancer ) before advanced or metastatic non-small cell lung cancer that is not affecting the squamous cells . '</li><li>'If it is , it will be treated as an operator , if it is not , it will be treated as a user function . '</li></ul> | |
| | 6 | <ul><li>'3 -RRB- Republican congressional representatives , because of their belief in a minimalist state , are less willing to engage in local benefit-seeking than are Democratic members of Congress . '</li><li>'The idea would be to administer to patients the growth-controlling proteins made by healthy versions of the damaged genes . '</li><li>'That is the way the system works . '</li></ul> | |
| | 0 | <ul><li>'Prior to 1932 , the pattern was nearly the opposite . '</li><li>'Never in my life have I been so frightened . '</li><li>'Then your focus will go to an input text box where you can type your function . '</li></ul> | |
| | 1 | <ul><li>'Mr. Neuberger realized that , although of Italian ancestry , Mr. Mariotta still could qualify as a minority person since he was born in Puerto Rico . '</li><li>'But Dr. Vogelstein had yet to nail the identity of the gene that , if damaged , flipped a colon cell into full-blown malignancy . '</li><li>'Some found it on the screen of a personal computer . '</li></ul> | |
| | 5 | <ul><li>"On the Right , the tone was set by Jacques Chirac , who declared in 1976 that `` 900,000 unemployed would not become a problem in a country with 2 million of foreign workers , '' and on the Left by Michel Rocard explaining in 1990 that France `` can not accommodate all the world 's misery . '' "</li><li>"But the council 's program to attract and train ringers is only partly successful , says Mr. Baldwin . "</li><li>'The scientists say that since breast cancer often strikes multiple members of certain families , the gene , when inherited in a damaged form , may predispose women to the cancer . '</li></ul> | |
| | 2 | <ul><li>'It explains how the Committee for Medicinal Products for Veterinary Use ( CVMP ) assessed the studies performed , to reach their recommendations on how to use the medicine . '</li><li>'US banks repay state support '</li><li>'-- In most states , increasing expenditures on education , in our current circumstances , will probably make things worse , not better . '</li></ul> | |
|
|
| ## Evaluation |
|
|
| ### Metrics |
| | Label | Accuracy | |
| |:--------|:---------| |
| | **all** | 0.1272 | |
|
|
| ## 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-6") |
| # Run inference |
| preds = model("To break the uncomfortable silence , Haney began to talk . ") |
| ``` |
|
|
| <!-- |
| ### 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 | 4 | 25.0952 | 74 | |
|
|
| | Label | Training Sample Count | |
| |:------|:----------------------| |
| | 0 | 6 | |
| | 1 | 6 | |
| | 2 | 6 | |
| | 3 | 6 | |
| | 4 | 6 | |
| | 5 | 6 | |
| | 6 | 6 | |
|
|
| ### Training Hyperparameters |
| - batch_size: (16, 16) |
| - num_epochs: (2, 2) |
| - 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 | |
| |:------:|:----:|:-------------:|:---------------:| |
| | 0.0095 | 1 | 0.3696 | - | |
| | 0.4762 | 50 | 0.1725 | - | |
| | 0.9524 | 100 | 0.0204 | - | |
| | 1.4286 | 150 | 0.0051 | - | |
| | 1.9048 | 200 | 0.0037 | - | |
| |
| ### 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.* |
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