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	---
	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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