---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: I really enjoy beer logos and branding. I like Budweiser design and also the
modern Modelo can as well.
- text: Drinking for me is a big trigger for dissociation. I can do maybe two beers
before I start to slide. At that point, I don't feel the effects so I overdrink.
I don't drink wine or hard alcohol at all anymore. ... Personally I agree alcohol
isn't good for physical or mental health and it's a supremely negative drug. I
have respect for those that can avoid it altogether. Also limit it to no more
than 2 full beers across 72 hours and a week break after. I got to this point
after 6 mos sober & then joining a SMART recovery program because full abstinence
was too much for me to be successful. As a foodie, HSP, wine- & hop-head, it's
a lot for me to cut out entirely.
- text: 'Big time, because my ADHD is one of the 2 bigger drivers for my anxiety and
depression. So when I would drink especially when I had taken anti depressants
that day, I would get to the point where i had suicidal thoughts over the smallest
things, which mind you the last time I drank, just a month and change before I
went to a mental hospital because of it. And this was me like 2 beers in. My doctor
at the hospital told me something that I have hugged onto: suicide can happen
at any time. I had thought about it but I stopped because of my wife and family.
When I was drinking, I didn’t think about any of them.'
- text: That and going out is expensive. I’d much rather knock back a couple of beers
and play Switch. Cheaper that way, plus I don’t end up smelling like an ashtray.
- text: By my house pizza is pretty inexpensive. I might be able to get two cheap
beers too!
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/paraphrase-mpnet-base-v2
---
# 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 [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:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 3 classes
### 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 | - 'I was spending too much money on beer and it wasn’t helping my life in any capacity, so I cut it out. I have enough other expensive hobbies I don’t need liver damage to be one of them.'
- "And I forgot the worst: eating out is expensive, and beer is crazy expensive. That's really annoying."
- 'Young me also didn’t realize a few ballpark beers could have you reevaluating your monthly budget'
|
| 2 | - 'Mental health problems and obesity often go hand in hand. In particular depression can be countered through endorphines released through simple Workouts including (!) normal paced walking outside. I do factor these things in. But if you eat unhealthy, only sit at home in the shadow, smoke tobacco or even worse weed with the occasional beer, you do not give yourself a fighting chance. There are exceptions. Yes.'
- 'It\'s also essential for vitamin d Alcohol isn\'t essential and has no positive health outcomes. Even when you consider "getting together with the boys" as a positive mental health aspect, it\'s negated by all the other effects. I still have a few beer a week, but I\'m aware of its consequences'
- "I drink on SSRI but I know two things. If I drink a lot the other day my anxiety is hell and I have to double the dose of my anti anxiety meds so I do it only if I don't have to do anything important the other day and veeeery occasionally. If I occasionally drink one or two beers yes it hits me more hard, I used to be that kind of person who needed a lot of alcohol to feel the high and now with one glass of wine I feel it, but I don't have any problem the other day. Be careful because the first time I found out the first thing I hated myself, I had to sleep all day to get through the hangxiety"
|
| 0 | - "I'm not sure if that's actually true (maybe I'm wrong) cause with the exception of the occasional craft brew, I always found the alcohol level to be the same on both sides of the border. Budweiser down there and Molson up here are both 5%."
- '?? angolbryggeri - Hazy Crazy\n\n✴️ IPA\n\n?? Sweden ????\n\n??Abv 6.5%\n\n⭐️ 3.60 / 5.0 ~ avg 3.67\n\n?? systembolaget\n\n#beer #bier #birra #öl #cerveza #øl #craftbeer #ipa #dipa #tipa #sour #gose #berlinerweisse #paleale #pilsner #lager #stout #beeroftheday #beerphotografy #hantverksöl #untappd #beergeek #beerlover #ilovebeer #cheers #beerstagram #instabeer #beerporn #ängöl #sweden'
- 'Lately some popular breweries around me have catered to lighter beers away from mostly pales les. Hefeweizens, Pilsner’s, blondes, and it’s been really nice. My local had 3 awesome pilsners that taste straight out of Europe.'
|
## 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("bhaskars113/beer-budget-health-model")
# Run inference
preds = model("By my house pizza is pretty inexpensive. I might be able to get two cheap beers too!")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 12 | 50.7391 | 177 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 16 |
| 1 | 15 |
| 2 | 15 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0087 | 1 | 0.203 | - |
| 0.4348 | 50 | 0.003 | - |
| 0.8696 | 100 | 0.0007 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.3.1
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.17.1
- 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}
}
```