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	---
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	widget:
	- text: Federal Office for Radiation Protection established a new monitoring network
	around nuclear waste storage sites.
	- text: how could we imagine these mechanisms to be implemented within a nato-based
	missile defence system.
	- text: president putin said that the precondition for a ceasefire is that ukraine
	should give up even more land, to give up all the four provinces that russia has
	annexed without controlling.
	- text: and helped protect and defend turkey’s territory and citizens against missile
	attacks.
	- text: let me first of all say that we take nuclear issues extremely seriously.
	metrics:
	- f1_macro
	- f1_binary
	pipeline_tag: text-classification
	library_name: setfit
	inference: true
	base_model: codefuse-ai/F2LLM-v2-80M
	model-index:
	- name: SetFit with codefuse-ai/F2LLM-v2-80M
	results:
	- task:
	type: text-classification
	name: Text Classification
	dataset:
	name: Unknown
	type: unknown
	split: test
	metrics:
	- type: f1_macro
	value: 0.9169073916737468
	name: F1_Macro
	- type: f1_binary
	value: 0.9065420560747663
	name: F1_Binary
	---

	# SetFit with codefuse-ai/F2LLM-v2-80M

	This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [codefuse-ai/F2LLM-v2-80M](https://huggingface.co/codefuse-ai/F2LLM-v2-80M) 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: [codefuse-ai/F2LLM-v2-80M](https://huggingface.co/codefuse-ai/F2LLM-v2-80M)
	- Classification head: a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
	- Maximum Sequence Length: 40960 tokens
	- Number of Classes: 2 classes
	<!-- - Training Dataset: [Unknown](https://huggingface.co/datasets/unknown) -->
	<!-- - 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 \|
	\|:---------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| positive \| <ul><li>'actually, we have decided in nato at the last summit that we should explore the possibilities to integrate russian missile defence systems in our missile defence system, which i think has become even more easy after the u.s. has presented new missile defence plans.'</li><li>'and we see, of course, the risk of proliferation of nuclear weapons.'</li><li>'there are of course opportunities and we need to engage with china on issues like climate change, arms control.'</li></ul> \|
	\| negative \| <ul><li>'We welcome the successful achievement of a draft Chemical Weapons Convention.'</li><li>'in practice, this means that, in addition to reinforcing cooperation with our current partners, we should look to enhance our relations with countries such as australia , new zealand , japan and south korea.'</li><li>'this is not about militarizing space.'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| F1_Macro \| F1_Binary \|
	\|:--------\|:---------\|:----------\|
	\| all \| 0.9169 \| 0.9065 \|

	## 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("fefofico/nuclear_trained_f2llm_temp")
	# Run inference
	preds = model("let me first of all say that we take nuclear issues extremely seriously.")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:--------\|:----\|
	\| Word count \| 2 \| 23.7926 \| 132 \|

	\| Label \| Training Sample Count \|
	\|:---------\|:----------------------\|
	\| negative \| 1096 \|
	\| positive \| 857 \|

	### Training Hyperparameters
	- batch_size: (128, 128)
	- num_epochs: (1, 1)
	- max_steps: -1
	- sampling_strategy: oversampling
	- num_iterations: 20
	- body_learning_rate: (5e-07, 5e-07)
	- head_learning_rate: 0.0002
	- loss: CosineSimilarityLoss
	- distance_metric: cosine_distance
	- margin: 0.25
	- end_to_end: False
	- use_amp: False
	- warmup_proportion: 0.1
	- l2_weight: 0.35
	- seed: 42
	- eval_max_steps: -1
	- load_best_model_at_end: False

	### Training Results
	\| Epoch \| Step \| Training Loss \| Validation Loss \|
	\|:------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0016 \| 1 \| 0.3497 \| - \|
	\| 0.0327 \| 20 \| 0.3955 \| - \|
	\| 0.0655 \| 40 \| 0.3678 \| - \|
	\| 0.0982 \| 60 \| 0.3743 \| - \|
	\| 0.1309 \| 80 \| 0.3384 \| - \|
	\| 0.1637 \| 100 \| 0.3244 \| - \|
	\| 0.1964 \| 120 \| 0.3087 \| - \|
	\| 0.2291 \| 140 \| 0.29 \| - \|
	\| 0.2619 \| 160 \| 0.2675 \| - \|
	\| 0.2946 \| 180 \| 0.2659 \| - \|
	\| 0.3273 \| 200 \| 0.2584 \| - \|
	\| 0.3601 \| 220 \| 0.2573 \| - \|
	\| 0.3928 \| 240 \| 0.2544 \| - \|
	\| 0.4255 \| 260 \| 0.2541 \| - \|
	\| 0.4583 \| 280 \| 0.2498 \| - \|
	\| 0.4910 \| 300 \| 0.2492 \| - \|
	\| 0.5237 \| 320 \| 0.2471 \| - \|
	\| 0.5565 \| 340 \| 0.2397 \| - \|
	\| 0.5892 \| 360 \| 0.2356 \| - \|
	\| 0.6219 \| 380 \| 0.2287 \| - \|
	\| 0.6547 \| 400 \| 0.2277 \| - \|
	\| 0.6874 \| 420 \| 0.223 \| - \|
	\| 0.7201 \| 440 \| 0.2169 \| - \|
	\| 0.7529 \| 460 \| 0.2154 \| - \|
	\| 0.7856 \| 480 \| 0.2067 \| - \|
	\| 0.8183 \| 500 \| 0.2084 \| - \|
	\| 0.8511 \| 520 \| 0.1983 \| - \|
	\| 0.8838 \| 540 \| 0.199 \| - \|
	\| 0.9165 \| 560 \| 0.1999 \| - \|
	\| 0.9493 \| 580 \| 0.1939 \| - \|
	\| 0.9820 \| 600 \| 0.1909 \| - \|
	\| 1.0 \| 611 \| - \| 0.1728 \|
	\| 1.0147 \| 620 \| 0.1827 \| - \|
	\| 1.0475 \| 640 \| 0.1736 \| - \|
	\| 1.0802 \| 660 \| 0.1788 \| - \|
	\| 1.1129 \| 680 \| 0.1741 \| - \|
	\| 1.1457 \| 700 \| 0.1731 \| - \|
	\| 1.1784 \| 720 \| 0.1734 \| - \|
	\| 1.2111 \| 740 \| 0.1645 \| - \|
	\| 1.2439 \| 760 \| 0.1679 \| - \|
	\| 1.2766 \| 780 \| 0.1602 \| - \|
	\| 1.3093 \| 800 \| 0.1525 \| - \|
	\| 1.3421 \| 820 \| 0.1519 \| - \|
	\| 1.3748 \| 840 \| 0.1563 \| - \|
	\| 1.4075 \| 860 \| 0.1564 \| - \|
	\| 1.4403 \| 880 \| 0.1502 \| - \|
	\| 1.4730 \| 900 \| 0.144 \| - \|
	\| 1.5057 \| 920 \| 0.1479 \| - \|
	\| 1.5385 \| 940 \| 0.1472 \| - \|
	\| 1.5712 \| 960 \| 0.1461 \| - \|
	\| 1.6039 \| 980 \| 0.137 \| - \|
	\| 1.6367 \| 1000 \| 0.1497 \| - \|
	\| 1.6694 \| 1020 \| 0.1433 \| - \|
	\| 1.7021 \| 1040 \| 0.1426 \| - \|
	\| 1.7349 \| 1060 \| 0.1345 \| - \|
	\| 1.7676 \| 1080 \| 0.1406 \| - \|
	\| 1.8003 \| 1100 \| 0.135 \| - \|
	\| 1.8331 \| 1120 \| 0.1434 \| - \|
	\| 1.8658 \| 1140 \| 0.1407 \| - \|
	\| 1.8985 \| 1160 \| 0.1353 \| - \|
	\| 1.9313 \| 1180 \| 0.133 \| - \|
	\| 1.9640 \| 1200 \| 0.1326 \| - \|
	\| 1.9967 \| 1220 \| 0.1357 \| - \|
	\| 2.0 \| 1222 \| - \| 0.1313 \|
	\| 0.0016 \| 1 \| 0.1361 \| - \|
	\| 0.0327 \| 20 \| 0.1349 \| - \|
	\| 0.0655 \| 40 \| 0.1338 \| - \|
	\| 0.0982 \| 60 \| 0.1338 \| - \|
	\| 0.1309 \| 80 \| 0.1412 \| - \|
	\| 0.1637 \| 100 \| 0.1269 \| - \|
	\| 0.1964 \| 120 \| 0.1213 \| - \|
	\| 0.2291 \| 140 \| 0.1266 \| - \|
	\| 0.2619 \| 160 \| 0.1239 \| - \|
	\| 0.2946 \| 180 \| 0.1162 \| - \|
	\| 0.3273 \| 200 \| 0.1121 \| - \|
	\| 0.3601 \| 220 \| 0.1136 \| - \|
	\| 0.3928 \| 240 \| 0.111 \| - \|
	\| 0.4255 \| 260 \| 0.11 \| - \|
	\| 0.4583 \| 280 \| 0.1091 \| - \|
	\| 0.4910 \| 300 \| 0.1009 \| - \|
	\| 0.5237 \| 320 \| 0.0963 \| - \|
	\| 0.5565 \| 340 \| 0.094 \| - \|
	\| 0.5892 \| 360 \| 0.1001 \| - \|
	\| 0.6219 \| 380 \| 0.0956 \| - \|
	\| 0.6547 \| 400 \| 0.0889 \| - \|
	\| 0.6874 \| 420 \| 0.0895 \| - \|
	\| 0.7201 \| 440 \| 0.0934 \| - \|
	\| 0.7529 \| 460 \| 0.0857 \| - \|
	\| 0.7856 \| 480 \| 0.0882 \| - \|
	\| 0.8183 \| 500 \| 0.0878 \| - \|
	\| 0.8511 \| 520 \| 0.0878 \| - \|
	\| 0.8838 \| 540 \| 0.0909 \| - \|
	\| 0.9165 \| 560 \| 0.0928 \| - \|
	\| 0.9493 \| 580 \| 0.0903 \| - \|
	\| 0.9820 \| 600 \| 0.0925 \| - \|
	\| 1.0 \| 611 \| - \| 0.1090 \|

	### Framework Versions
	- Python: 3.12.13
	- SetFit: 1.1.3
	- Sentence Transformers: 3.4.1
	- Transformers: 4.57.6
	- PyTorch: 2.11.0+cu128
	- Datasets: 5.0.0
	- Tokenizers: 0.22.2

	## 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}
	}
	```

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