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---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: Omar-Nasr/setfitmodel
metrics:
- accuracy
widget:
- text: ' I like art and nature but you can’t really talk about those for more than
a few seconds'
- text: ' That''s kind of the nature of my volunteer work, but you could volunteer
with a food bank or boys and girls club, which would involve more social interaction
Just breaking that cycle by going for a short walk around the neighbourhood is
a good idea'
- text: ' That being heat, sweat, more people outside and you wear less clothes on
you (not so comfortable being a fat guy and sweat can be seen on your shirt)'
- text: ' Want to join soccer club but so scared'
- text: ' I literally do not leave the house, I will sometimes go in the garden but
no further So I just ran outside and back to the car'
pipeline_tag: text-classification
inference: true
model-index:
- name: SetFit with Omar-Nasr/setfitmodel
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.5866666666666667
name: Accuracy
---
# SetFit with Omar-Nasr/setfitmodel
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [Omar-Nasr/setfitmodel](https://huggingface.co/Omar-Nasr/setfitmodel) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) 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:** [Omar-Nasr/setfitmodel](https://huggingface.co/Omar-Nasr/setfitmodel)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 256 tokens
- **Number of Classes:** 4 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 |
|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1.0 | <ul><li>' Go out for a walk once a day additionally and slowly start increasing the time you spend outside Go out for a walk once a day additionally and slowly start increasing the time you spend outside Start doing sport, either outdoors or at a gym If you can, try to take your dog to a dog park or something like that'</li><li>' Try challenging yourself more, take a walk in the park, small things like that make you better Try challenging yourself more, take a walk in the park, small things like that make you better '</li><li>" Now I'm not saying to go to a party on the spot, just go out, shop, take a walk in the park, that kind of thing Now I'm not saying to go to a party on the spot, just go out, shop, take a walk in the park, that kind of thing"</li></ul> |
| 2.0 | <ul><li>' I’m an equestrian, so I ride horses and manage for a pretty famous trainer I can hold a non work related conversation with a stranger while I’m working but if I met that same person outside of the work day I’d have a panic attack and not be able to say a word'</li><li>' On long walks to errands, and whilst power walking for exercise'</li><li>' She said no, but we have a tasty forest fruit mix cake I felt high as a kite walking home'</li></ul> |
| 0.0 | <ul><li>' Good to know that some people are in the same camp'</li><li>" I'm sure if the worlds ever did clash that your friends would understand (few people actually enjoy being at work) and, worst case scenario, your coworkers would be surprised at your outgoing nature while around friends"</li><li>' If anything you should be thinking about wearing sun screen so you retain your good skin as it becomes your ally as you age outside'</li></ul> |
| 3.0 | <ul><li>" While I ended up making progress, it wasn't as fast as I had hoped and I still had a lot of trouble doing some things (such as jogging in public)"</li><li>' One, frack you guys who say “just get over it”, you’ve probably never dealt with anxiety, it’s like you are carrying the weight of everyone’s judgements and eyes on you with every possibility of any and every event running through your head all the time I am trying, I force myself outside and to interact but it’s terrifying and people just don’t seem to get that'</li><li>" I want to go swimming, anxiety and low self esteem make it really hard I want to go swimming, anxiety and low self esteem make it really hard I'm at least planning to go for a swim at a nearby lake but there is one problem I have: I'm not really confident with my body"</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.5867 |
## 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("Omar-Nasr/setfitmodel")
# Run inference
preds = model(" Want to join soccer club but so scared")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:-----|
| Word count | 4 | 51.2656 | 1083 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0.0 | 16 |
| 1.0 | 16 |
| 2.0 | 16 |
| 3.0 | 16 |
### Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0026 | 1 | 0.0 | - |
| 0.1302 | 50 | 0.0001 | - |
| 0.2604 | 100 | 0.0 | - |
| 0.3906 | 150 | 0.0 | - |
| 0.5208 | 200 | 0.0 | - |
| 0.6510 | 250 | 0.0 | - |
| 0.7812 | 300 | 0.0 | - |
| 0.9115 | 350 | 0.0 | - |
### Framework Versions
- Python: 3.10.13
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.39.3
- PyTorch: 2.1.2
- Datasets: 2.18.0
- Tokenizers: 0.15.2
## 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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