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---
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: 0.17086092715231788
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
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### 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 |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 3 | <ul><li>'Saves the log with all results into an HTML file to be able to print or publish . '</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>"It was the most exercise we 'd had all morning and it was followed by our driving immediately to the nearest watering hole . "</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>'Here , the experience of New York City is decisive . '</li><li>'The idea would be to administer to patients the growth-controlling proteins made by healthy versions of the damaged genes . '</li></ul> |
| 2 | <ul><li>'-- Students should move up the educational ladder as their academic potential allows . '</li><li>'The next day , Sunday , the hangover reminded Haney where he had been the night before . '</li><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></ul> |
| 0 | <ul><li>'A minor contrast to Costa Rica , comparing the 22 players called by both countries for the friendly game today , at 3:05 pm at the National Stadium in San Jose . '</li><li>'Never in my life have I been so frightened . '</li><li>'Prior to 1932 , the pattern was nearly the opposite . '</li></ul> |
| 5 | <ul><li>'2 -RRB- Congressional representatives have two basic responsibilities while voting in office -- dealing with national issues -LRB- programmatic actions such as casting roll call votes on legislation that imposes costs and/or confers benefits on the population at large -RRB- and attending to local issues -LRB- constituency service and pork barrel -RRB- . '</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><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></ul> |
| 4 | <ul><li>'Researchers say the inactivation of tumor-suppressor genes , alone or in combination , appears crucial to the development of such scourges as cancer of the brain , the skin , kidney , prostate , and cervix . '</li><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>'`` To ring for even one service at this tower , we have to scrape , `` says Mr. Hammond , a retired water-authority worker . `` '</li></ul> |
| 1 | <ul><li>"kalgebra 's console is useful as a calculator . "</li><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>"Biggest trouble was scared family who could n't get a phone line through , and spent a really horrible hour not knowing . "</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.1709 |
## 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-8")
# Run inference
preds = model("To break the uncomfortable silence , Haney began to talk . ")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 4 | 27.0 | 74 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 8 |
| 1 | 8 |
| 2 | 8 |
| 3 | 8 |
| 4 | 8 |
| 5 | 8 |
| 6 | 8 |
### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0071 | 1 | 0.2786 | - |
| 0.3571 | 50 | 0.1703 | - |
| 0.7143 | 100 | 0.0932 | - |
| 1.0714 | 150 | 0.0173 | - |
| 1.4286 | 200 | 0.0048 | - |
| 1.7857 | 250 | 0.0024 | - |
### 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}
}
```
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