---
base_model: Alibaba-NLP/gte-base-en-v1.5
datasets:
- diwank/hn-upvote-data
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 'Title: Pixar’s Rules of Storytelling
Source: b''aerogrammestudio.com'''
- text: 'Title: What I''ve learned about Open Source community over 30 years
Source: b'''''
- text: 'Title: My Python code is a neural network
Source: b'''''
- text: 'Title: The telltale words that could identify generative AI text
Source: b'''''
- text: 'Title: What I''ve learned about Open Source community over 30 years
Source: b'''''
inference: true
---
# SetFit with Alibaba-NLP/gte-base-en-v1.5
This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [diwank/hn-upvote-data](https://huggingface.co/datasets/diwank/hn-upvote-data) dataset that can be used for Text Classification. This SetFit model uses [Alibaba-NLP/gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-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:** [Alibaba-NLP/gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 8192 tokens
- **Number of Classes:** 2 classes
- **Training Dataset:** [diwank/hn-upvote-data](https://huggingface.co/datasets/diwank/hn-upvote-data)
### 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 | - "Title: The telltale words that could identify generative AI text\nSource: b''"
- "Title: What I've learned about Open Source community over 30 years\nSource: b''"
- "Title: My Python code is a neural network\nSource: b''"
|
| 1 | - "Title: Rat Park Experiment: A New Theory of Addiction\nSource: b'sub.garrytan.com'"
- "Title: Thinking the unthinkable\nSource: b'anarchistsoccermom.blogspot.com'"
- "Title: Realtime Analysis of the Oroville Dam Disaster\nSource: b'github.com'"
|
## 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("diwank/hn-upvote-classifier")
# Run inference
preds = model("Title: My Python code is a neural network
Source: b''")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 3 | 10.2389 | 20 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 3302 |
| 1 | 1114 |
### Training Hyperparameters
- batch_size: (256, 16)
- num_epochs: (1, 16)
- max_steps: -1
- sampling_strategy: undersampling
- body_learning_rate: (4e-05, 2e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: True
- use_amp: True
- warmup_proportion: 0.05
- l2_weight: 0.2
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:----------:|:---------:|:-------------:|:---------------:|
| 0.0000 | 1 | 0.1861 | - |
| 0.0017 | 50 | 0.1334 | - |
| 0.0035 | 100 | 0.0344 | - |
| 0.0052 | 150 | 0.0048 | - |
| 0.0070 | 200 | 0.0027 | - |
| 0.0087 | 250 | 0.002 | - |
| 0.0104 | 300 | 0.0016 | - |
| 0.0122 | 350 | 0.0011 | - |
| 0.0139 | 400 | 0.001 | - |
| 0.0157 | 450 | 0.0009 | - |
| 0.0174 | 500 | 0.0008 | - |
| 0.0191 | 550 | 0.0006 | - |
| 0.0209 | 600 | 0.0006 | - |
| 0.0226 | 650 | 0.0006 | - |
| 0.0244 | 700 | 0.0005 | - |
| 0.0261 | 750 | 0.0005 | - |
| 0.0278 | 800 | 0.0004 | - |
| 0.0296 | 850 | 0.0004 | - |
| 0.0313 | 900 | 0.0004 | - |
| 0.0331 | 950 | 0.0003 | - |
| 0.0348 | 1000 | 0.0003 | - |
| 0.0365 | 1050 | 0.0003 | - |
| 0.0383 | 1100 | 0.0002 | - |
| 0.0400 | 1150 | 0.0002 | - |
| 0.0418 | 1200 | 0.0002 | - |
| 0.0435 | 1250 | 0.0002 | - |
| 0.0452 | 1300 | 0.0002 | - |
| 0.0470 | 1350 | 0.0002 | - |
| 0.0487 | 1400 | 0.0002 | - |
| 0.0505 | 1450 | 0.0001 | - |
| 0.0522 | 1500 | 0.0001 | - |
| 0.0539 | 1550 | 0.0001 | - |
| 0.0557 | 1600 | 0.0001 | - |
| 0.0574 | 1650 | 0.0001 | - |
| 0.0592 | 1700 | 0.0001 | - |
| 0.0609 | 1750 | 0.0001 | - |
| 0.0626 | 1800 | 0.0001 | - |
| 0.0644 | 1850 | 0.0001 | - |
| 0.0661 | 1900 | 0.0001 | - |
| 0.0679 | 1950 | 0.0001 | - |
| 0.0696 | 2000 | 0.0001 | - |
| 0.0713 | 2050 | 0.0001 | - |
| 0.0731 | 2100 | 0.0001 | - |
| 0.0748 | 2150 | 0.0001 | - |
| 0.0766 | 2200 | 0.0001 | - |
| 0.0783 | 2250 | 0.0001 | - |
| 0.0800 | 2300 | 0.0001 | - |
| 0.0818 | 2350 | 0.0001 | - |
| 0.0835 | 2400 | 0.0001 | - |
| 0.0853 | 2450 | 0.0001 | - |
| 0.0870 | 2500 | 0.0001 | - |
| 0.0887 | 2550 | 0.0001 | - |
| 0.0905 | 2600 | 0.0001 | - |
| 0.0922 | 2650 | 0.0001 | - |
| 0.0940 | 2700 | 0.0 | - |
| 0.0957 | 2750 | 0.0001 | - |
| 0.0974 | 2800 | 0.0001 | - |
| 0.0992 | 2850 | 0.0001 | - |
| 0.1009 | 2900 | 0.0001 | - |
| 0.1027 | 2950 | 0.0001 | - |
| 0.1044 | 3000 | 0.0001 | 0.0 |
| 0.1061 | 3050 | 0.0001 | - |
| 0.1079 | 3100 | 0.0001 | - |
| 0.1096 | 3150 | 0.0001 | - |
| 0.1114 | 3200 | 0.0001 | - |
| 0.1131 | 3250 | 0.0 | - |
| 0.1148 | 3300 | 0.0 | - |
| 0.1166 | 3350 | 0.0 | - |
| 0.1183 | 3400 | 0.0 | - |
| 0.1201 | 3450 | 0.0 | - |
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| 0.1340 | 3850 | 0.0 | - |
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| 0.1374 | 3950 | 0.0 | - |
| 0.1392 | 4000 | 0.0 | - |
| 0.1409 | 4050 | 0.0 | - |
| 0.1427 | 4100 | 0.0 | - |
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| 0.2018 | 5800 | 0.0 | - |
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| 0.2088 | 6000 | 0.0 | 0.0 |
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| 0.2784 | 8000 | 0.0 | - |
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| 0.3132 | 9000 | 0.0 | 0.0 |
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| 0.6907 | 19850 | 0.0 | - |
| 0.6925 | 19900 | 0.0 | - |
| 0.6942 | 19950 | 0.0 | - |
| 0.6959 | 20000 | 0.0 | - |
| 0.6977 | 20050 | 0.0 | - |
| 0.6994 | 20100 | 0.0 | - |
| 0.7012 | 20150 | 0.0 | - |
| 0.7029 | 20200 | 0.0 | - |
| 0.7046 | 20250 | 0.0 | - |
| 0.7064 | 20300 | 0.0 | - |
| 0.7081 | 20350 | 0.0 | - |
| 0.7099 | 20400 | 0.0 | - |
| 0.7116 | 20450 | 0.0 | - |
| 0.7133 | 20500 | 0.0 | - |
| 0.7151 | 20550 | 0.0 | - |
| 0.7168 | 20600 | 0.0 | - |
| 0.7186 | 20650 | 0.0 | - |
| 0.7203 | 20700 | 0.0 | - |
| 0.7220 | 20750 | 0.0 | - |
| 0.7238 | 20800 | 0.0 | - |
| 0.7255 | 20850 | 0.0 | - |
| 0.7273 | 20900 | 0.0 | - |
| 0.7290 | 20950 | 0.0 | - |
| **0.7307** | **21000** | **0.0** | **0.0** |
| 0.7325 | 21050 | 0.0 | - |
| 0.7342 | 21100 | 0.0 | - |
| 0.7360 | 21150 | 0.0 | - |
| 0.7377 | 21200 | 0.0 | - |
| 0.7394 | 21250 | 0.0 | - |
| 0.7412 | 21300 | 0.0 | - |
| 0.7429 | 21350 | 0.0 | - |
| 0.7447 | 21400 | 0.0 | - |
| 0.7464 | 21450 | 0.0 | - |
| 0.7481 | 21500 | 0.0 | - |
| 0.7499 | 21550 | 0.0 | - |
| 0.7516 | 21600 | 0.0 | - |
| 0.7534 | 21650 | 0.0 | - |
| 0.7551 | 21700 | 0.0 | - |
| 0.7568 | 21750 | 0.0 | - |
| 0.7586 | 21800 | 0.0 | - |
| 0.7603 | 21850 | 0.0 | - |
| 0.7621 | 21900 | 0.0 | - |
| 0.7638 | 21950 | 0.0 | - |
| 0.7655 | 22000 | 0.0 | - |
| 0.7673 | 22050 | 0.0 | - |
| 0.7690 | 22100 | 0.0 | - |
| 0.7708 | 22150 | 0.0 | - |
| 0.7725 | 22200 | 0.0 | - |
| 0.7742 | 22250 | 0.0 | - |
| 0.7760 | 22300 | 0.0 | - |
| 0.7777 | 22350 | 0.0 | - |
| 0.7795 | 22400 | 0.0 | - |
| 0.7812 | 22450 | 0.0 | - |
| 0.7829 | 22500 | 0.0 | - |
| 0.7847 | 22550 | 0.0 | - |
| 0.7864 | 22600 | 0.0 | - |
| 0.7882 | 22650 | 0.0 | - |
| 0.7899 | 22700 | 0.0 | - |
| 0.7916 | 22750 | 0.0 | - |
| 0.7934 | 22800 | 0.0 | - |
| 0.7951 | 22850 | 0.0 | - |
| 0.7969 | 22900 | 0.0 | - |
| 0.7986 | 22950 | 0.0 | - |
| 0.8003 | 23000 | 0.0 | - |
| 0.8021 | 23050 | 0.0 | - |
| 0.8038 | 23100 | 0.0 | - |
| 0.8056 | 23150 | 0.0 | - |
| 0.8073 | 23200 | 0.0 | - |
| 0.8090 | 23250 | 0.0 | - |
| 0.8108 | 23300 | 0.0 | - |
| 0.8125 | 23350 | 0.0 | - |
| 0.8143 | 23400 | 0.0 | - |
| 0.8160 | 23450 | 0.0 | - |
| 0.8177 | 23500 | 0.0 | - |
| 0.8195 | 23550 | 0.0 | - |
| 0.8212 | 23600 | 0.0 | - |
| 0.8230 | 23650 | 0.0 | - |
| 0.8247 | 23700 | 0.0 | - |
| 0.8264 | 23750 | 0.0 | - |
| 0.8282 | 23800 | 0.0 | - |
| 0.8299 | 23850 | 0.0 | - |
| 0.8317 | 23900 | 0.0 | - |
| 0.8334 | 23950 | 0.0 | - |
| 0.8351 | 24000 | 0.0 | 0.0 |
| 0.8369 | 24050 | 0.0 | - |
| 0.8386 | 24100 | 0.0 | - |
| 0.8404 | 24150 | 0.0 | - |
| 0.8421 | 24200 | 0.0 | - |
| 0.8438 | 24250 | 0.0 | - |
| 0.8456 | 24300 | 0.0 | - |
| 0.8473 | 24350 | 0.0 | - |
| 0.8491 | 24400 | 0.0 | - |
| 0.8508 | 24450 | 0.0 | - |
| 0.8525 | 24500 | 0.0 | - |
| 0.8543 | 24550 | 0.0 | - |
| 0.8560 | 24600 | 0.0 | - |
| 0.8577 | 24650 | 0.0 | - |
| 0.8595 | 24700 | 0.0 | - |
| 0.8612 | 24750 | 0.0 | - |
| 0.8630 | 24800 | 0.0 | - |
| 0.8647 | 24850 | 0.0 | - |
| 0.8664 | 24900 | 0.0 | - |
| 0.8682 | 24950 | 0.0 | - |
| 0.8699 | 25000 | 0.0 | - |
| 0.8717 | 25050 | 0.0 | - |
| 0.8734 | 25100 | 0.0 | - |
| 0.8751 | 25150 | 0.0 | - |
| 0.8769 | 25200 | 0.0 | - |
| 0.8786 | 25250 | 0.0 | - |
| 0.8804 | 25300 | 0.0 | - |
| 0.8821 | 25350 | 0.0 | - |
| 0.8838 | 25400 | 0.0 | - |
| 0.8856 | 25450 | 0.0 | - |
| 0.8873 | 25500 | 0.0 | - |
| 0.8891 | 25550 | 0.0 | - |
| 0.8908 | 25600 | 0.0 | - |
| 0.8925 | 25650 | 0.0 | - |
| 0.8943 | 25700 | 0.0 | - |
| 0.8960 | 25750 | 0.0 | - |
| 0.8978 | 25800 | 0.0 | - |
| 0.8995 | 25850 | 0.0 | - |
| 0.9012 | 25900 | 0.0 | - |
| 0.9030 | 25950 | 0.0 | - |
| 0.9047 | 26000 | 0.0 | - |
| 0.9065 | 26050 | 0.0 | - |
| 0.9082 | 26100 | 0.0 | - |
| 0.9099 | 26150 | 0.0 | - |
| 0.9117 | 26200 | 0.0 | - |
| 0.9134 | 26250 | 0.0 | - |
| 0.9152 | 26300 | 0.0 | - |
| 0.9169 | 26350 | 0.0 | - |
| 0.9186 | 26400 | 0.0 | - |
| 0.9204 | 26450 | 0.0 | - |
| 0.9221 | 26500 | 0.0 | - |
| 0.9239 | 26550 | 0.0 | - |
| 0.9256 | 26600 | 0.0 | - |
| 0.9273 | 26650 | 0.0 | - |
| 0.9291 | 26700 | 0.0 | - |
| 0.9308 | 26750 | 0.0 | - |
| 0.9326 | 26800 | 0.0 | - |
| 0.9343 | 26850 | 0.0 | - |
| 0.9360 | 26900 | 0.0 | - |
| 0.9378 | 26950 | 0.0 | - |
| 0.9395 | 27000 | 0.0 | 0.0 |
| 0.9413 | 27050 | 0.0 | - |
| 0.9430 | 27100 | 0.0 | - |
| 0.9447 | 27150 | 0.0 | - |
| 0.9465 | 27200 | 0.0 | - |
| 0.9482 | 27250 | 0.0 | - |
| 0.9500 | 27300 | 0.0 | - |
| 0.9517 | 27350 | 0.0 | - |
| 0.9534 | 27400 | 0.0 | - |
| 0.9552 | 27450 | 0.0 | - |
| 0.9569 | 27500 | 0.0 | - |
| 0.9587 | 27550 | 0.0 | - |
| 0.9604 | 27600 | 0.0 | - |
| 0.9621 | 27650 | 0.0 | - |
| 0.9639 | 27700 | 0.0 | - |
| 0.9656 | 27750 | 0.0 | - |
| 0.9674 | 27800 | 0.0 | - |
| 0.9691 | 27850 | 0.0 | - |
| 0.9708 | 27900 | 0.0 | - |
| 0.9726 | 27950 | 0.0 | - |
| 0.9743 | 28000 | 0.0 | - |
| 0.9761 | 28050 | 0.0 | - |
| 0.9778 | 28100 | 0.0 | - |
| 0.9795 | 28150 | 0.0 | - |
| 0.9813 | 28200 | 0.0 | - |
| 0.9830 | 28250 | 0.0 | - |
| 0.9848 | 28300 | 0.0 | - |
| 0.9865 | 28350 | 0.0 | - |
| 0.9882 | 28400 | 0.0 | - |
| 0.9900 | 28450 | 0.0 | - |
| 0.9917 | 28500 | 0.0 | - |
| 0.9935 | 28550 | 0.0 | - |
| 0.9952 | 28600 | 0.0 | - |
| 0.9969 | 28650 | 0.0 | - |
| 0.9987 | 28700 | 0.0 | - |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.14
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.3.1+cu121
- Datasets: 2.20.0
- 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}
}
```