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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: 0.9105329220295713
	name: F1_Macro
	- type: f1_binary
	value: 0.8988235294117647
	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 \| 0.9105 \| 0.8988 \|

	## 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")
	# 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: (2, 2)
	- max_steps: -1
	- sampling_strategy: oversampling
	- num_iterations: 20
	- body_learning_rate: (1e-07, 1e-07)
	- head_learning_rate: 0.0001
	- 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 \| 0.3957 \| - \|
	\| 0.0655 \| 40 \| 0.3689 \| - \|
	\| 0.0982 \| 60 \| 0.3778 \| - \|
	\| 0.1309 \| 80 \| 0.3446 \| - \|
	\| 0.1637 \| 100 \| 0.3345 \| - \|
	\| 0.1964 \| 120 \| 0.3217 \| - \|
	\| 0.2291 \| 140 \| 0.3038 \| - \|
	\| 0.2619 \| 160 \| 0.2764 \| - \|
	\| 0.2946 \| 180 \| 0.27 \| - \|
	\| 0.3273 \| 200 \| 0.2596 \| - \|
	\| 0.3601 \| 220 \| 0.2575 \| - \|
	\| 0.3928 \| 240 \| 0.2528 \| - \|
	\| 0.4255 \| 260 \| 0.2507 \| - \|
	\| 0.4583 \| 280 \| 0.2427 \| - \|
	\| 0.4910 \| 300 \| 0.2347 \| - \|
	\| 0.5237 \| 320 \| 0.2226 \| - \|
	\| 0.5565 \| 340 \| 0.2094 \| - \|
	\| 0.5892 \| 360 \| 0.1975 \| - \|
	\| 0.6219 \| 380 \| 0.1837 \| - \|
	\| 0.6547 \| 400 \| 0.1729 \| - \|
	\| 0.6874 \| 420 \| 0.1646 \| - \|
	\| 0.7201 \| 440 \| 0.1516 \| - \|
	\| 0.7529 \| 460 \| 0.1392 \| - \|
	\| 0.7856 \| 480 \| 0.1303 \| - \|
	\| 0.8183 \| 500 \| 0.1265 \| - \|
	\| 0.8511 \| 520 \| 0.1156 \| - \|
	\| 0.8838 \| 540 \| 0.1149 \| - \|
	\| 0.9165 \| 560 \| 0.112 \| - \|
	\| 0.9493 \| 580 \| 0.1041 \| - \|
	\| 0.9820 \| 600 \| 0.1007 \| - \|
	\| 1.0 \| 611 \| - \| 0.1073 \|
	\| 1.0147 \| 620 \| 0.0926 \| - \|
	\| 1.0475 \| 640 \| 0.076 \| - \|
	\| 1.0802 \| 660 \| 0.0862 \| - \|
	\| 1.1129 \| 680 \| 0.078 \| - \|
	\| 1.1457 \| 700 \| 0.0745 \| - \|
	\| 1.1784 \| 720 \| 0.0676 \| - \|
	\| 1.2111 \| 740 \| 0.0524 \| - \|
	\| 1.2439 \| 760 \| 0.0585 \| - \|
	\| 1.2766 \| 780 \| 0.0506 \| - \|
	\| 1.3093 \| 800 \| 0.0419 \| - \|
	\| 1.3421 \| 820 \| 0.0446 \| - \|
	\| 1.3748 \| 840 \| 0.04 \| - \|
	\| 1.4075 \| 860 \| 0.0349 \| - \|
	\| 1.4403 \| 880 \| 0.0353 \| - \|
	\| 1.4730 \| 900 \| 0.0259 \| - \|
	\| 1.5057 \| 920 \| 0.0273 \| - \|
	\| 1.5385 \| 940 \| 0.0252 \| - \|
	\| 1.5712 \| 960 \| 0.0247 \| - \|
	\| 1.6039 \| 980 \| 0.0157 \| - \|
	\| 1.6367 \| 1000 \| 0.0172 \| - \|
	\| 1.6694 \| 1020 \| 0.0142 \| - \|
	\| 1.7021 \| 1040 \| 0.0136 \| - \|
	\| 1.7349 \| 1060 \| 0.0144 \| - \|
	\| 1.7676 \| 1080 \| 0.0111 \| - \|
	\| 1.8003 \| 1100 \| 0.0074 \| - \|
	\| 1.8331 \| 1120 \| 0.0103 \| - \|
	\| 1.8658 \| 1140 \| 0.0118 \| - \|
	\| 1.8985 \| 1160 \| 0.0098 \| - \|
	\| 1.9313 \| 1180 \| 0.0071 \| - \|
	\| 1.9640 \| 1200 \| 0.0082 \| - \|
	\| 1.9967 \| 1220 \| 0.0092 \| - \|
	\| 2.0 \| 1222 \| - \| 0.1244 \|
	\| 2.0295 \| 1240 \| 0.0049 \| - \|
	\| 2.0622 \| 1260 \| 0.0063 \| - \|
	\| 2.0949 \| 1280 \| 0.0047 \| - \|
	\| 2.1277 \| 1300 \| 0.0062 \| - \|
	\| 2.1604 \| 1320 \| 0.0053 \| - \|
	\| 2.1931 \| 1340 \| 0.0048 \| - \|
	\| 2.2259 \| 1360 \| 0.0042 \| - \|
	\| 2.2586 \| 1380 \| 0.0046 \| - \|
	\| 2.2913 \| 1400 \| 0.0053 \| - \|
	\| 2.3241 \| 1420 \| 0.007 \| - \|
	\| 2.3568 \| 1440 \| 0.0063 \| - \|
	\| 2.3895 \| 1460 \| 0.0044 \| - \|
	\| 2.4223 \| 1480 \| 0.0047 \| - \|
	\| 2.4550 \| 1500 \| 0.0033 \| - \|
	\| 2.4877 \| 1520 \| 0.0039 \| - \|
	\| 2.5205 \| 1540 \| 0.0069 \| - \|
	\| 2.5532 \| 1560 \| 0.004 \| - \|
	\| 2.5859 \| 1580 \| 0.0038 \| - \|
	\| 2.6187 \| 1600 \| 0.0031 \| - \|
	\| 2.6514 \| 1620 \| 0.005 \| - \|
	\| 2.6841 \| 1640 \| 0.0028 \| - \|
	\| 2.7169 \| 1660 \| 0.0056 \| - \|
	\| 2.7496 \| 1680 \| 0.0056 \| - \|
	\| 2.7823 \| 1700 \| 0.005 \| - \|
	\| 2.8151 \| 1720 \| 0.0045 \| - \|
	\| 2.8478 \| 1740 \| 0.0038 \| - \|
	\| 2.8805 \| 1760 \| 0.0049 \| - \|
	\| 2.9133 \| 1780 \| 0.0051 \| - \|
	\| 2.9460 \| 1800 \| 0.0031 \| - \|
	\| 2.9787 \| 1820 \| 0.0021 \| - \|
	\| 3.0 \| 1833 \| - \| 0.1320 \|
	\| 3.0115 \| 1840 \| 0.0027 \| - \|
	\| 3.0442 \| 1860 \| 0.005 \| - \|
	\| 3.0769 \| 1880 \| 0.004 \| - \|
	\| 3.1097 \| 1900 \| 0.0031 \| - \|
	\| 3.1424 \| 1920 \| 0.0032 \| - \|
	\| 3.1751 \| 1940 \| 0.0038 \| - \|
	\| 3.2079 \| 1960 \| 0.0045 \| - \|
	\| 3.2406 \| 1980 \| 0.0031 \| - \|
	\| 3.2733 \| 2000 \| 0.0044 \| - \|
	\| 3.3061 \| 2020 \| 0.0047 \| - \|
	\| 3.3388 \| 2040 \| 0.0022 \| - \|
	\| 3.3715 \| 2060 \| 0.0023 \| - \|
	\| 3.4043 \| 2080 \| 0.0027 \| - \|
	\| 3.4370 \| 2100 \| 0.0038 \| - \|
	\| 3.4697 \| 2120 \| 0.0011 \| - \|
	\| 3.5025 \| 2140 \| 0.0042 \| - \|
	\| 3.5352 \| 2160 \| 0.0027 \| - \|
	\| 3.5679 \| 2180 \| 0.0033 \| - \|
	\| 3.6007 \| 2200 \| 0.0042 \| - \|
	\| 3.6334 \| 2220 \| 0.0036 \| - \|
	\| 3.6661 \| 2240 \| 0.0046 \| - \|
	\| 3.6989 \| 2260 \| 0.0029 \| - \|
	\| 3.7316 \| 2280 \| 0.0041 \| - \|
	\| 3.7643 \| 2300 \| 0.003 \| - \|
	\| 3.7971 \| 2320 \| 0.0033 \| - \|
	\| 3.8298 \| 2340 \| 0.0033 \| - \|
	\| 3.8625 \| 2360 \| 0.0047 \| - \|
	\| 3.8953 \| 2380 \| 0.0041 \| - \|
	\| 3.9280 \| 2400 \| 0.0036 \| - \|
	\| 3.9607 \| 2420 \| 0.0037 \| - \|
	\| 3.9935 \| 2440 \| 0.0044 \| - \|
	\| 4.0 \| 2444 \| - \| 0.1335 \|
	\| 4.0262 \| 2460 \| 0.0034 \| - \|
	\| 4.0589 \| 2480 \| 0.0036 \| - \|
	\| 4.0917 \| 2500 \| 0.0041 \| - \|
	\| 4.1244 \| 2520 \| 0.0021 \| - \|
	\| 4.1571 \| 2540 \| 0.0032 \| - \|
	\| 4.1899 \| 2560 \| 0.002 \| - \|
	\| 4.2226 \| 2580 \| 0.0039 \| - \|
	\| 4.2553 \| 2600 \| 0.0035 \| - \|
	\| 4.2881 \| 2620 \| 0.0032 \| - \|
	\| 4.3208 \| 2640 \| 0.0032 \| - \|
	\| 4.3535 \| 2660 \| 0.0025 \| - \|
	\| 4.3863 \| 2680 \| 0.0024 \| - \|
	\| 4.4190 \| 2700 \| 0.0054 \| - \|
	\| 4.4517 \| 2720 \| 0.0035 \| - \|
	\| 4.4845 \| 2740 \| 0.0028 \| - \|
	\| 4.5172 \| 2760 \| 0.0042 \| - \|
	\| 4.5499 \| 2780 \| 0.0025 \| - \|
	\| 4.5827 \| 2800 \| 0.0027 \| - \|
	\| 4.6154 \| 2820 \| 0.0039 \| - \|
	\| 4.6481 \| 2840 \| 0.0046 \| - \|
	\| 4.6809 \| 2860 \| 0.0036 \| - \|
	\| 4.7136 \| 2880 \| 0.004 \| - \|
	\| 4.7463 \| 2900 \| 0.0031 \| - \|
	\| 4.7791 \| 2920 \| 0.0024 \| - \|
	\| 4.8118 \| 2940 \| 0.0036 \| - \|
	\| 4.8445 \| 2960 \| 0.0046 \| - \|
	\| 4.8773 \| 2980 \| 0.0025 \| - \|
	\| 4.9100 \| 3000 \| 0.0056 \| - \|
	\| 4.9427 \| 3020 \| 0.0031 \| - \|
	\| 4.9755 \| 3040 \| 0.0024 \| - \|
	\| 5.0 \| 3055 \| - \| 0.1357 \|
	\| 0.0016 \| 1 \| 0.008 \| - \|
	\| 0.0327 \| 20 \| 0.004 \| - \|
	\| 0.0655 \| 40 \| 0.0028 \| - \|
	\| 0.0982 \| 60 \| 0.0042 \| - \|
	\| 0.1309 \| 80 \| 0.0024 \| - \|
	\| 0.1637 \| 100 \| 0.0039 \| - \|
	\| 0.1964 \| 120 \| 0.0028 \| - \|
	\| 0.2291 \| 140 \| 0.0053 \| - \|
	\| 0.2619 \| 160 \| 0.0027 \| - \|
	\| 0.2946 \| 180 \| 0.0046 \| - \|
	\| 0.3273 \| 200 \| 0.0031 \| - \|
	\| 0.3601 \| 220 \| 0.0031 \| - \|
	\| 0.3928 \| 240 \| 0.0045 \| - \|
	\| 0.4255 \| 260 \| 0.0028 \| - \|
	\| 0.4583 \| 280 \| 0.0045 \| - \|
	\| 0.4910 \| 300 \| 0.0034 \| - \|
	\| 0.5237 \| 320 \| 0.0019 \| - \|
	\| 0.5565 \| 340 \| 0.0008 \| - \|
	\| 0.5892 \| 360 \| 0.0035 \| - \|
	\| 0.6219 \| 380 \| 0.0033 \| - \|
	\| 0.6547 \| 400 \| 0.0026 \| - \|
	\| 0.6874 \| 420 \| 0.0027 \| - \|
	\| 0.7201 \| 440 \| 0.0034 \| - \|
	\| 0.7529 \| 460 \| 0.0033 \| - \|
	\| 0.7856 \| 480 \| 0.0019 \| - \|
	\| 0.8183 \| 500 \| 0.0036 \| - \|
	\| 0.8511 \| 520 \| 0.0023 \| - \|
	\| 0.8838 \| 540 \| 0.0026 \| - \|
	\| 0.9165 \| 560 \| 0.0033 \| - \|
	\| 0.9493 \| 580 \| 0.0028 \| - \|
	\| 0.9820 \| 600 \| 0.004 \| - \|
	\| 1.0 \| 611 \| - \| 0.1421 \|
	\| 1.0147 \| 620 \| 0.003 \| - \|
	\| 1.0475 \| 640 \| 0.0023 \| - \|
	\| 1.0802 \| 660 \| 0.0019 \| - \|
	\| 1.1129 \| 680 \| 0.0026 \| - \|
	\| 1.1457 \| 700 \| 0.0018 \| - \|
	\| 1.1784 \| 720 \| 0.0018 \| - \|
	\| 1.2111 \| 740 \| 0.0007 \| - \|
	\| 1.2439 \| 760 \| 0.0025 \| - \|
	\| 1.2766 \| 780 \| 0.0021 \| - \|
	\| 1.3093 \| 800 \| 0.0011 \| - \|
	\| 1.3421 \| 820 \| 0.0029 \| - \|
	\| 1.3748 \| 840 \| 0.0023 \| - \|
	\| 1.4075 \| 860 \| 0.0019 \| - \|
	\| 1.4403 \| 880 \| 0.0016 \| - \|
	\| 1.4730 \| 900 \| 0.0022 \| - \|
	\| 1.5057 \| 920 \| 0.0015 \| - \|
	\| 1.5385 \| 940 \| 0.0012 \| - \|
	\| 1.5712 \| 960 \| 0.0014 \| - \|
	\| 1.6039 \| 980 \| 0.0012 \| - \|
	\| 1.6367 \| 1000 \| 0.0019 \| - \|
	\| 1.6694 \| 1020 \| 0.0015 \| - \|
	\| 1.7021 \| 1040 \| 0.0015 \| - \|
	\| 1.7349 \| 1060 \| 0.0008 \| - \|
	\| 1.7676 \| 1080 \| 0.0004 \| - \|
	\| 1.8003 \| 1100 \| 0.0014 \| - \|
	\| 1.8331 \| 1120 \| 0.0015 \| - \|
	\| 1.8658 \| 1140 \| 0.0011 \| - \|
	\| 1.8985 \| 1160 \| 0.0008 \| - \|
	\| 1.9313 \| 1180 \| 0.0021 \| - \|
	\| 1.9640 \| 1200 \| 0.0011 \| - \|
	\| 1.9967 \| 1220 \| 0.0023 \| - \|
	\| 2.0 \| 1222 \| - \| 0.1374 \|
	\| 0.0016 \| 1 \| 0.0009 \| - \|
	\| 0.0327 \| 20 \| 0.0022 \| - \|
	\| 0.0655 \| 40 \| 0.0015 \| - \|
	\| 0.0982 \| 60 \| 0.0022 \| - \|
	\| 0.1309 \| 80 \| 0.0007 \| - \|
	\| 0.1637 \| 100 \| 0.0015 \| - \|
	\| 0.1964 \| 120 \| 0.0011 \| - \|
	\| 0.2291 \| 140 \| 0.0019 \| - \|
	\| 0.2619 \| 160 \| 0.0011 \| - \|
	\| 0.2946 \| 180 \| 0.0014 \| - \|
	\| 0.3273 \| 200 \| 0.0011 \| - \|
	\| 0.3601 \| 220 \| 0.0007 \| - \|
	\| 0.3928 \| 240 \| 0.0014 \| - \|
	\| 0.4255 \| 260 \| 0.0011 \| - \|
	\| 0.4583 \| 280 \| 0.0024 \| - \|
	\| 0.4910 \| 300 \| 0.0011 \| - \|
	\| 0.5237 \| 320 \| 0.0006 \| - \|
	\| 0.5565 \| 340 \| 0.0003 \| - \|
	\| 0.5892 \| 360 \| 0.0003 \| - \|
	\| 0.6219 \| 380 \| 0.001 \| - \|
	\| 0.6547 \| 400 \| 0.0006 \| - \|
	\| 0.6874 \| 420 \| 0.0014 \| - \|
	\| 0.7201 \| 440 \| 0.001 \| - \|
	\| 0.7529 \| 460 \| 0.001 \| - \|
	\| 0.7856 \| 480 \| 0.0006 \| - \|
	\| 0.8183 \| 500 \| 0.0016 \| - \|
	\| 0.8511 \| 520 \| 0.0007 \| - \|
	\| 0.8838 \| 540 \| 0.0011 \| - \|
	\| 0.9165 \| 560 \| 0.0005 \| - \|
	\| 0.9493 \| 580 \| 0.0008 \| - \|
	\| 0.9820 \| 600 \| 0.0004 \| - \|
	\| 1.0 \| 611 \| - \| 0.1352 \|
	\| 1.0147 \| 620 \| 0.0003 \| - \|
	\| 1.0475 \| 640 \| 0.0003 \| - \|
	\| 1.0802 \| 660 \| 0.0003 \| - \|
	\| 1.1129 \| 680 \| 0.0003 \| - \|
	\| 1.1457 \| 700 \| 0.0003 \| - \|
	\| 1.1784 \| 720 \| 0.0003 \| - \|
	\| 1.2111 \| 740 \| 0.0002 \| - \|
	\| 1.2439 \| 760 \| 0.0003 \| - \|
	\| 1.2766 \| 780 \| 0.0003 \| - \|
	\| 1.3093 \| 800 \| 0.0002 \| - \|
	\| 1.3421 \| 820 \| 0.0003 \| - \|
	\| 1.3748 \| 840 \| 0.0003 \| - \|
	\| 1.4075 \| 860 \| 0.0003 \| - \|
	\| 1.4403 \| 880 \| 0.0003 \| - \|
	\| 1.4730 \| 900 \| 0.0003 \| - \|
	\| 1.5057 \| 920 \| 0.0003 \| - \|
	\| 1.5385 \| 940 \| 0.0003 \| - \|
	\| 1.5712 \| 960 \| 0.0003 \| - \|
	\| 1.6039 \| 980 \| 0.0003 \| - \|
	\| 1.6367 \| 1000 \| 0.0003 \| - \|
	\| 1.6694 \| 1020 \| 0.0002 \| - \|
	\| 1.7021 \| 1040 \| 0.0002 \| - \|
	\| 1.7349 \| 1060 \| 0.0002 \| - \|
	\| 1.7676 \| 1080 \| 0.0002 \| - \|
	\| 1.8003 \| 1100 \| 0.0002 \| - \|
	\| 1.8331 \| 1120 \| 0.0002 \| - \|
	\| 1.8658 \| 1140 \| 0.0002 \| - \|
	\| 1.8985 \| 1160 \| 0.0003 \| - \|
	\| 1.9313 \| 1180 \| 0.0003 \| - \|
	\| 1.9640 \| 1200 \| 0.0002 \| - \|
	\| 1.9967 \| 1220 \| 0.0003 \| - \|
	\| 2.0 \| 1222 \| - \| 0.1392 \|

	### 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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