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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 Alavas worked themselves to the bone in the last period , and English |
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and San Emeterio ( 65-75 ) had already made it clear that they were not going |
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to let anyone take away what they had earned during the first thirty minutes . ' |
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- text: 'To break the uncomfortable silence , Haney began to talk . ' |
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- text: 'For the treatment of non-small cell lung cancer , the effects of Alimta were |
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compared with those of docetaxel ( another anticancer medicine ) in one study |
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involving 571 patients with locally advanced or metastatic disease who had received |
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chemotherapy in the past . ' |
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- text: 'As we all know , a few minutes before the end of the game ( that their team |
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had already won ) , both players deliberately wasted time which made the referee |
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show the second yellow card to both of them . ' |
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- text: 'In contrast , patients whose cancer was affecting squamous cells had shorter |
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survival times if they received Alimta . ' |
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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: 0.1271523178807947 |
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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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| 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> | |
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| 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> | |
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| 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> | |
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| 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> | |
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| 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> | |
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| 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> | |
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| 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> | |
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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** | 0.1272 | |
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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-class-6") |
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# Run inference |
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preds = model("To break the uncomfortable silence , Haney began to talk . ") |
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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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*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 | 4 | 25.0952 | 74 | |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| 0 | 6 | |
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| 1 | 6 | |
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| 2 | 6 | |
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| 3 | 6 | |
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| 4 | 6 | |
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| 5 | 6 | |
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| 6 | 6 | |
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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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| 0.0095 | 1 | 0.3696 | - | |
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| 0.4762 | 50 | 0.1725 | - | |
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| 0.9524 | 100 | 0.0204 | - | |
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| 1.4286 | 150 | 0.0051 | - | |
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| 1.9048 | 200 | 0.0037 | - | |
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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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