setfit safetensors new sentence-transformers text-classification generated_from_setfit_trainer custom_code dataset:diwank/hn-upvote-data arxiv:2209.11055 base_model:Alibaba-NLP/gte-base-en-v1.5 base_model:finetune:Alibaba-NLP/gte-base-en-v1.5 region:us
---
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: My Python code is a neural network
- text: The telltale words that could identify generative AI text
- text: My Python code is a neural network
- text: My Python code is a neural network
- text: The telltale words that could identify generative AI text
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)
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### 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>'The telltale words that could identify generative AI text'</li><li>'The telltale words that could identify generative AI text'</li><li>'The telltale words that could identify generative AI text'</li></ul>                        |
| 1     | <ul><li>'Dangerous Feelings\nSource: www.collaborativefund.com'</li><li>'The Modos Paper Monitor\nSource: www.modos.tech'</li><li>'What did Mary know? A thought experiment about consciousness (2013)\nSource: philosophynow.org'</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("diwank/hn-upvote-classifier")
# Run inference
preds = model("My Python code is a neural network")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 3   | 8.6577 | 18  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 4577                  |
| 1     | 252                   |

### Training Hyperparameters
- batch_size: (320, 32)
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0001 | 1    | 0.208         | -               |
| 0.0069 | 50   | 0.0121        | -               |
| 0.0139 | 100  | 0.002         | -               |
| 0.0208 | 150  | 0.0032        | -               |
| 0.0277 | 200  | 0.001         | -               |
| 0.0347 | 250  | 0.0006        | -               |
| 0.0416 | 300  | 0.0005        | -               |
| 0.0486 | 350  | 0.0004        | -               |
| 0.0555 | 400  | 0.0003        | -               |
| 0.0624 | 450  | 0.0002        | -               |
| 0.0694 | 500  | 0.0002        | -               |
| 0.0763 | 550  | 0.0002        | -               |
| 0.0832 | 600  | 0.0002        | -               |
| 0.0902 | 650  | 0.0001        | -               |
| 0.0971 | 700  | 0.0001        | -               |
| 0.1040 | 750  | 0.0001        | -               |
| 0.1110 | 800  | 0.0001        | -               |
| 0.1179 | 850  | 0.0001        | -               |
| 0.1248 | 900  | 0.0001        | -               |
| 0.1318 | 950  | 0.0001        | -               |
| 0.1387 | 1000 | 0.0001        | -               |
| 0.1457 | 1050 | 0.0001        | -               |
| 0.1526 | 1100 | 0.0001        | -               |
| 0.1595 | 1150 | 0.0001        | -               |
| 0.1665 | 1200 | 0.0001        | -               |
| 0.1734 | 1250 | 0.0001        | -               |
| 0.1803 | 1300 | 0.0001        | -               |
| 0.1873 | 1350 | 0.0001        | -               |
| 0.1942 | 1400 | 0.0001        | -               |
| 0.2011 | 1450 | 0.0001        | -               |
| 0.2081 | 1500 | 0.0001        | -               |
| 0.2150 | 1550 | 0.0001        | -               |
| 0.2219 | 1600 | 0.0           | -               |
| 0.2289 | 1650 | 0.0           | -               |
| 0.2358 | 1700 | 0.0           | -               |
| 0.2428 | 1750 | 0.0           | -               |
| 0.2497 | 1800 | 0.0001        | -               |
| 0.2566 | 1850 | 0.0           | -               |
| 0.2636 | 1900 | 0.0           | -               |
| 0.2705 | 1950 | 0.0           | -               |
| 0.2774 | 2000 | 0.0           | -               |
| 0.2844 | 2050 | 0.0           | -               |
| 0.2913 | 2100 | 0.0           | -               |
| 0.2982 | 2150 | 0.0           | -               |
| 0.3052 | 2200 | 0.0           | -               |
| 0.3121 | 2250 | 0.0           | -               |
| 0.3190 | 2300 | 0.0           | -               |
| 0.3260 | 2350 | 0.0           | -               |
| 0.3329 | 2400 | 0.0           | -               |
| 0.3399 | 2450 | 0.0           | -               |
| 0.3468 | 2500 | 0.0           | -               |
| 0.3537 | 2550 | 0.0           | -               |
| 0.3607 | 2600 | 0.0           | -               |
| 0.3676 | 2650 | 0.0           | -               |
| 0.3745 | 2700 | 0.0           | -               |
| 0.3815 | 2750 | 0.0           | -               |
| 0.3884 | 2800 | 0.0           | -               |
| 0.3953 | 2850 | 0.0           | -               |
| 0.4023 | 2900 | 0.0           | -               |
| 0.4092 | 2950 | 0.0           | -               |
| 0.4161 | 3000 | 0.0           | -               |
| 0.4231 | 3050 | 0.0           | -               |
| 0.4300 | 3100 | 0.0           | -               |
| 0.4370 | 3150 | 0.0           | -               |
| 0.4439 | 3200 | 0.0           | -               |
| 0.4508 | 3250 | 0.0           | -               |
| 0.4578 | 3300 | 0.0           | -               |
| 0.4647 | 3350 | 0.0           | -               |
| 0.4716 | 3400 | 0.0           | -               |
| 0.4786 | 3450 | 0.0           | -               |
| 0.4855 | 3500 | 0.0           | -               |
| 0.4924 | 3550 | 0.0           | -               |
| 0.4994 | 3600 | 0.0           | -               |
| 0.5063 | 3650 | 0.0           | -               |
| 0.5132 | 3700 | 0.0           | -               |
| 0.5202 | 3750 | 0.0           | -               |
| 0.5271 | 3800 | 0.0           | -               |
| 0.5341 | 3850 | 0.0           | -               |
| 0.5410 | 3900 | 0.0           | -               |
| 0.5479 | 3950 | 0.0           | -               |
| 0.5549 | 4000 | 0.0           | -               |
| 0.5618 | 4050 | 0.0           | -               |
| 0.5687 | 4100 | 0.0           | -               |
| 0.5757 | 4150 | 0.0           | -               |
| 0.5826 | 4200 | 0.0           | -               |
| 0.5895 | 4250 | 0.0           | -               |
| 0.5965 | 4300 | 0.0           | -               |
| 0.6034 | 4350 | 0.0           | -               |
| 0.6103 | 4400 | 0.0           | -               |
| 0.6173 | 4450 | 0.0           | -               |
| 0.6242 | 4500 | 0.0           | -               |
| 0.6312 | 4550 | 0.0           | -               |
| 0.6381 | 4600 | 0.0           | -               |
| 0.6450 | 4650 | 0.0           | -               |
| 0.6520 | 4700 | 0.0           | -               |
| 0.6589 | 4750 | 0.0           | -               |
| 0.6658 | 4800 | 0.0           | -               |
| 0.6728 | 4850 | 0.0           | -               |
| 0.6797 | 4900 | 0.0           | -               |
| 0.6866 | 4950 | 0.0           | -               |
| 0.6936 | 5000 | 0.0           | -               |
| 0.7005 | 5050 | 0.0           | -               |
| 0.7074 | 5100 | 0.0           | -               |
| 0.7144 | 5150 | 0.0           | -               |
| 0.7213 | 5200 | 0.0           | -               |
| 0.7283 | 5250 | 0.0           | -               |
| 0.7352 | 5300 | 0.0           | -               |
| 0.7421 | 5350 | 0.0           | -               |
| 0.7491 | 5400 | 0.0           | -               |
| 0.7560 | 5450 | 0.0           | -               |
| 0.7629 | 5500 | 0.0           | -               |
| 0.7699 | 5550 | 0.0           | -               |
| 0.7768 | 5600 | 0.0           | -               |
| 0.7837 | 5650 | 0.0           | -               |
| 0.7907 | 5700 | 0.0           | -               |
| 0.7976 | 5750 | 0.0           | -               |
| 0.8045 | 5800 | 0.0           | -               |
| 0.8115 | 5850 | 0.0           | -               |
| 0.8184 | 5900 | 0.0           | -               |
| 0.8254 | 5950 | 0.0           | -               |
| 0.8323 | 6000 | 0.0           | -               |
| 0.8392 | 6050 | 0.0           | -               |
| 0.8462 | 6100 | 0.0           | -               |
| 0.8531 | 6150 | 0.0           | -               |
| 0.8600 | 6200 | 0.0           | -               |
| 0.8670 | 6250 | 0.0           | -               |
| 0.8739 | 6300 | 0.0           | -               |
| 0.8808 | 6350 | 0.0           | -               |
| 0.8878 | 6400 | 0.0           | -               |
| 0.8947 | 6450 | 0.0           | -               |
| 0.9017 | 6500 | 0.0           | -               |
| 0.9086 | 6550 | 0.0           | -               |
| 0.9155 | 6600 | 0.0           | -               |
| 0.9225 | 6650 | 0.0           | -               |
| 0.9294 | 6700 | 0.0           | -               |
| 0.9363 | 6750 | 0.0           | -               |
| 0.9433 | 6800 | 0.0           | -               |
| 0.9502 | 6850 | 0.0           | -               |
| 0.9571 | 6900 | 0.0           | -               |
| 0.9641 | 6950 | 0.0           | -               |
| 0.9710 | 7000 | 0.0           | -               |
| 0.9779 | 7050 | 0.0           | -               |
| 0.9849 | 7100 | 0.0           | -               |
| 0.9918 | 7150 | 0.0           | -               |
| 0.9988 | 7200 | 0.0           | -               |

### 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}
}
```

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