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---
base_model: BAAI/bge-small-en-v1.5
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 'I have owned this NAS for almost a year now and actually purchased a second
one It works flawlessly and QNAP live tech support is superb There is also a fairly
comprehensive forum for users as well I have slowly upgraded my capacities as
newer larger capacity drives have come out on the market All have been recognized
and the space expanded without a hitch I highly recommend this product '
- text: Good as expected
- text: 'This is a very good video editing package In the past I ve only used Corel
video editing products but Cyberlink s offering is on par It offers similar options
but they are different enough for me to want to use both products depending on
what I m trying to achieve There are quick uploading options that make it very
easy to get video onto Youtube and other online video sites '
- text: Works great
- text: 'This is my favorite crack open the computer and amuse myself for a few hours
software Easy to pick up if you have no prior experience with computer animation
but advanced enough that someone with the right skills could pull together an
impressive movie '
inference: true
---
# SetFit with BAAI/bge-small-en-v1.5
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) 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:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5)
- **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:** 2 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 |
|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | <ul><li>'Have used Turbo Tax for years Never a problem I m pretty concerned now with the news that many of their users had their returns hacked by people who gained access to Turbo Tax and stole the information Not sure I will use it next year until I research how serious this is was '</li><li>'Can t beat an Apple computer Like P KB best by test '</li><li>'Works for Mac or Pc but not on widows '</li></ul> |
| 1 | <ul><li>'Would not install activation code not accepted Returned it '</li><li>'Worth all four of the software programs which are included in this product '</li><li>'The marketing information makes this software look like it should be fabulous lots of useful features that I would love to experiment with However the software just doesn t work I will keep using my very old JASC version of this software instead '</li></ul> |
## 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("selina09/yt_setfit")
# Run inference
preds = model("Works great")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 1 | 34.9207 | 102 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 123 |
| 1 | 41 |
### Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (10, 10)
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0019 | 1 | 0.2503 | - |
| 0.0942 | 50 | 0.2406 | - |
| 0.1883 | 100 | 0.2029 | - |
| 0.2825 | 150 | 0.2207 | - |
| 0.3766 | 200 | 0.1612 | - |
| 0.4708 | 250 | 0.0725 | - |
| 0.5650 | 300 | 0.0163 | - |
| 0.6591 | 350 | 0.0108 | - |
| 0.7533 | 400 | 0.0153 | - |
| 0.8475 | 450 | 0.0486 | - |
| 0.9416 | 500 | 0.0191 | - |
| 1.0358 | 550 | 0.0207 | - |
| 1.1299 | 600 | 0.0148 | - |
| 1.2241 | 650 | 0.0031 | - |
| 1.3183 | 700 | 0.001 | - |
| 1.4124 | 750 | 0.0287 | - |
| 1.5066 | 800 | 0.0146 | - |
| 1.6008 | 850 | 0.0147 | - |
| 1.6949 | 900 | 0.0165 | - |
| 1.7891 | 950 | 0.0008 | - |
| 1.8832 | 1000 | 0.0165 | - |
| 1.9774 | 1050 | 0.0007 | - |
| 2.0716 | 1100 | 0.0129 | - |
| 2.1657 | 1150 | 0.0143 | - |
| 2.2599 | 1200 | 0.0006 | - |
| 2.3540 | 1250 | 0.0008 | - |
| 2.4482 | 1300 | 0.0047 | - |
| 2.5424 | 1350 | 0.0005 | - |
| 2.6365 | 1400 | 0.0116 | - |
| 2.7307 | 1450 | 0.0093 | - |
| 2.8249 | 1500 | 0.0211 | - |
| 2.9190 | 1550 | 0.0076 | - |
| 3.0132 | 1600 | 0.0047 | - |
| 3.1073 | 1650 | 0.0005 | - |
| 3.2015 | 1700 | 0.0064 | - |
| 3.2957 | 1750 | 0.014 | - |
| 3.3898 | 1800 | 0.0479 | - |
| 3.4840 | 1850 | 0.0005 | - |
| 3.5782 | 1900 | 0.0045 | - |
| 3.6723 | 1950 | 0.0188 | - |
| 3.7665 | 2000 | 0.0004 | - |
| 3.8606 | 2050 | 0.0122 | - |
| 3.9548 | 2100 | 0.0004 | - |
| 4.0490 | 2150 | 0.008 | - |
| 4.1431 | 2200 | 0.0245 | - |
| 4.2373 | 2250 | 0.005 | - |
| 4.3315 | 2300 | 0.0244 | - |
| 4.4256 | 2350 | 0.0208 | - |
| 4.5198 | 2400 | 0.0237 | - |
| 4.6139 | 2450 | 0.0005 | - |
| 4.7081 | 2500 | 0.0004 | - |
| 4.8023 | 2550 | 0.02 | - |
| 4.8964 | 2600 | 0.0004 | - |
| 4.9906 | 2650 | 0.0067 | - |
| 5.0847 | 2700 | 0.0099 | - |
| 5.1789 | 2750 | 0.0138 | - |
| 5.2731 | 2800 | 0.0192 | - |
| 5.3672 | 2850 | 0.0217 | - |
| 5.4614 | 2900 | 0.0056 | - |
| 5.5556 | 2950 | 0.0003 | - |
| 5.6497 | 3000 | 0.0052 | - |
| 5.7439 | 3050 | 0.0123 | - |
| 5.8380 | 3100 | 0.0136 | - |
| 5.9322 | 3150 | 0.0221 | - |
| 6.0264 | 3200 | 0.0235 | - |
| 6.1205 | 3250 | 0.0144 | - |
| 6.2147 | 3300 | 0.0174 | - |
| 6.3089 | 3350 | 0.007 | - |
| 6.4030 | 3400 | 0.0044 | - |
| 6.4972 | 3450 | 0.0003 | - |
| 6.5913 | 3500 | 0.007 | - |
| 6.6855 | 3550 | 0.0004 | - |
| 6.7797 | 3600 | 0.0384 | - |
| 6.8738 | 3650 | 0.0055 | - |
| 6.9680 | 3700 | 0.0056 | - |
| 7.0621 | 3750 | 0.0118 | - |
| 7.1563 | 3800 | 0.0143 | - |
| 7.2505 | 3850 | 0.0289 | - |
| 7.3446 | 3900 | 0.0301 | - |
| 7.4388 | 3950 | 0.0119 | - |
| 7.5330 | 4000 | 0.012 | - |
| 7.6271 | 4050 | 0.0138 | - |
| 7.7213 | 4100 | 0.0148 | - |
| 7.8154 | 4150 | 0.0003 | - |
| 7.9096 | 4200 | 0.0268 | - |
| 8.0038 | 4250 | 0.0131 | - |
| 8.0979 | 4300 | 0.0237 | - |
| 8.1921 | 4350 | 0.0004 | - |
| 8.2863 | 4400 | 0.0211 | - |
| 8.3804 | 4450 | 0.0092 | - |
| 8.4746 | 4500 | 0.005 | - |
| 8.5687 | 4550 | 0.0056 | - |
| 8.6629 | 4600 | 0.0168 | - |
| 8.7571 | 4650 | 0.0045 | - |
| 8.8512 | 4700 | 0.0184 | - |
| 8.9454 | 4750 | 0.0049 | - |
| 9.0395 | 4800 | 0.0047 | - |
| 9.1337 | 4850 | 0.0099 | - |
| 9.2279 | 4900 | 0.0054 | - |
| 9.3220 | 4950 | 0.0185 | - |
| 9.4162 | 5000 | 0.005 | - |
| 9.5104 | 5050 | 0.0004 | - |
| 9.6045 | 5100 | 0.013 | - |
| 9.6987 | 5150 | 0.0002 | - |
| 9.7928 | 5200 | 0.0187 | - |
| 9.8870 | 5250 | 0.0003 | - |
| 9.9812 | 5300 | 0.0081 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- Transformers: 4.40.2
- PyTorch: 2.4.0+cu121
- Datasets: 2.21.0
- Tokenizers: 0.19.1
## 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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