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---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: I know you are searching for a flat to live for the whole next year .
- text: Dear sir Dimara .
- text: I have been doing Judo for the past 11 years with a lot of prizes .
- text: my village is the place where I live so I am trying to keep its environment
non - polluted and valid for life .
- text: I learnt from Research that you can do everything in anytime in addition a
little tired can change the life for the better .
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: 0.175
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:** 8 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
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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 |
|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1 | <ul><li>'I consider that is more convenient to drive a car because you carry on more things in your own car than travelling by car .'</li><li>'In the last few years forensic biology has developed many aspects like better sensibility , robustness of results and less time required for analyze a sample , but what struck me most is how fast this change happens .'</li><li>"The car is n't the best way for for the transport , because it produce much pollution , however the public transport is better to do a journey ."</li></ul> |
| 6 | <ul><li>'On the one hand travel by car are really much more convenient as give the chance to you to be independent .'</li><li>'When most people think about an important historical place in Italy , they think of Duomo , in Milano .'</li><li>'I like personality with childlike , so I like children .'</li></ul> |
| 5 | <ul><li>'Yours sincerely ,'</li><li>'This practice is considered those activities that anyone can do without any kind of special preparation .'</li><li>'Secondly , the public vehicle route are more far than usual route .'</li></ul> |
| 7 | <ul><li>'This conclusion become more prominent if we look into the data of the car companies and exponential growth in their sales figure and with low budget private cars in picture , scenario ddrastically changed in past 10 years'</li><li>'Recently I saw the thriller of mokingjay part 2 .'</li><li>"An example of that is the marriage of homosexual where some state admit this marriage , others do n't ."</li></ul> |
| 3 | <ul><li>'After that , the sports day began formally .'</li><li>'In those years I lived the worst moments in my life .'</li><li>'On the one hand , in my country there are a lot of place to travel .'</li></ul> |
| 2 | <ul><li>"Sharing houses or rooms have many advantages such as , cheap , safe , close to the university , and learn how to share everything with others . saving money and time will be more Obvious in university dormitories because monthly payments will be less than four times than hiring an apartment , and because it will be closer to the university , saving money and time is more efficient by reducing transportation 's costs"</li><li>'So , finally I suggest that it would be a great idea to combine the different types of activities , both popular and the newest .'</li><li>'Wszysycy residents of my village , they try to , so that our village was clear that pollute the environment as little as possible .'</li></ul> |
| 4 | <ul><li>'During summer I love to go to the beach and having sunbathing with my friends other than getting fun with them playing volleyball or run inside the water of the sea !'</li><li>'Jose is the best song . he is singing and talking in the party .'</li><li>"She fell sleep again , didn't she ?"</li></ul> |
| 0 | <ul><li>'I work for the same large company for 25 years , now is the time to change and find new job opportunities .'</li><li>'A problem which was caused by us , human beings , with their target of making money without thinking of the effects .'</li><li>'He was waiting 2 hours for her .'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.175 |
## 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/BEA2019-multi-class-20")
# Run inference
preds = model("Dear sir Dimara .")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 3 | 22.0 | 82 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 20 |
| 1 | 20 |
| 2 | 20 |
| 3 | 20 |
| 4 | 20 |
| 5 | 20 |
| 6 | 20 |
| 7 | 20 |
### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0025 | 1 | 0.3724 | - |
| 0.125 | 50 | 0.2732 | - |
| 0.25 | 100 | 0.3001 | - |
| 0.375 | 150 | 0.2525 | - |
| 0.5 | 200 | 0.1934 | - |
| 0.625 | 250 | 0.1164 | - |
| 0.75 | 300 | 0.0874 | - |
| 0.875 | 350 | 0.0624 | - |
| 1.0 | 400 | 0.052 | - |
| 1.125 | 450 | 0.0569 | - |
| 1.25 | 500 | 0.0248 | - |
| 1.375 | 550 | 0.0071 | - |
| 1.5 | 600 | 0.0124 | - |
| 1.625 | 650 | 0.0087 | - |
| 1.75 | 700 | 0.0086 | - |
| 1.875 | 750 | 0.066 | - |
| 2.0 | 800 | 0.0194 | - |
### 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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