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9c7171e verified 15 days ago

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	---
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	widget:
	- text: Our security policy balances the need for strong defenses with pursuit of
	peaceful international relations.
	- text: Economic diversification efforts have reduced our vulnerability to external
	shocks and market fluctuations.
	- text: If we fail to address the growing imbalances in international trade, our economic
	independence will be irreparably damaged.
	- text: The stories fit a pattern of 12 months of Serbian atrocities against Albanian
	civilians and seven years' Serbian aggression against its Balkan neighbours.
	- text: We managed to prevent a possible crisis.
	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: 0.8967545837827238
	name: F1_Macro
	- type: f1_binary
	value: 0.8755760368663594
	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 \|
	\|:---------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| negative \| <ul><li>'The more and the sooner we get the financial support we have requested, the sooner there will be peace.'</li><li>'The recent upgrade to the national power grid has significantly improved reliability, reducing the number of blackouts experienced by customers.'</li><li>'This is not an economic crisis.'</li></ul> \|
	\| positive \| <ul><li>'This problem is more complex than we initially understood.'</li><li>'Climate change is altering ocean currents, with unpredictable consequences for marine ecosystems.'</li><li>'The systematic underfunding of renewable energy research has left nations dangerously dependent on fossil fuels during global supply chain disruptions.'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| F1_Macro \| F1_Binary \|
	\|:--------\|:---------\|:----------\|
	\| all \| 0.8968 \| 0.8756 \|

	## 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/crisis_trained_f2llm_temp")
	# Run inference
	preds = model("We managed to prevent a possible crisis.")
	```

	<!--
	### Downstream Use

	List how someone could finetune this model on their own dataset.
	-->

	<!--
	### Out-of-Scope Use

	List how the model may foreseeably be misused and address what users ought not to do with the model.
	-->

	<!--
	## Bias, Risks and Limitations

	What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.
	-->

	<!--
	### Recommendations

	What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.
	-->

	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:--------\|:----\|
	\| Word count \| 1 \| 18.3023 \| 65 \|

	\| Label \| Training Sample Count \|
	\|:---------\|:----------------------\|
	\| negative \| 1307 \|
	\| positive \| 876 \|

	### Training Hyperparameters
	- batch_size: (128, 128)
	- num_epochs: (1, 1)
	- 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: 0.3
	- seed: 42
	- eval_max_steps: -1
	- load_best_model_at_end: False

	### Training Results
	\| Epoch \| Step \| Training Loss \| Validation Loss \|
	\|:------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0015 \| 1 \| 0.4239 \| - \|
	\| 0.0293 \| 20 \| 0.432 \| - \|
	\| 0.0586 \| 40 \| 0.4262 \| - \|
	\| 0.0878 \| 60 \| 0.4188 \| - \|
	\| 0.1171 \| 80 \| 0.4131 \| - \|
	\| 0.1464 \| 100 \| 0.4274 \| - \|
	\| 0.1757 \| 120 \| 0.4332 \| - \|
	\| 0.2050 \| 140 \| 0.4136 \| - \|
	\| 0.2343 \| 160 \| 0.428 \| - \|
	\| 0.2635 \| 180 \| 0.4066 \| - \|
	\| 0.2928 \| 200 \| 0.4064 \| - \|
	\| 0.3221 \| 220 \| 0.4144 \| - \|
	\| 0.3514 \| 240 \| 0.4072 \| - \|
	\| 0.3807 \| 260 \| 0.4008 \| - \|
	\| 0.4100 \| 280 \| 0.3918 \| - \|
	\| 0.4392 \| 300 \| 0.4047 \| - \|
	\| 0.4685 \| 320 \| 0.3979 \| - \|
	\| 0.4978 \| 340 \| 0.4005 \| - \|
	\| 0.5271 \| 360 \| 0.3825 \| - \|
	\| 0.5564 \| 380 \| 0.3728 \| - \|
	\| 0.5857 \| 400 \| 0.3666 \| - \|
	\| 0.6149 \| 420 \| 0.3737 \| - \|
	\| 0.6442 \| 440 \| 0.3626 \| - \|
	\| 0.6735 \| 460 \| 0.3456 \| - \|
	\| 0.7028 \| 480 \| 0.3565 \| - \|
	\| 0.7321 \| 500 \| 0.3476 \| - \|
	\| 0.7613 \| 520 \| 0.3409 \| - \|
	\| 0.7906 \| 540 \| 0.3481 \| - \|
	\| 0.8199 \| 560 \| 0.3298 \| - \|
	\| 0.8492 \| 580 \| 0.3303 \| - \|
	\| 0.8785 \| 600 \| 0.3257 \| - \|
	\| 0.9078 \| 620 \| 0.328 \| - \|
	\| 0.9370 \| 640 \| 0.3195 \| - \|
	\| 0.9663 \| 660 \| 0.3183 \| - \|
	\| 0.9956 \| 680 \| 0.3067 \| - \|
	\| 1.0 \| 683 \| - \| 0.3051 \|
	\| 1.0249 \| 700 \| 0.3067 \| - \|
	\| 1.0542 \| 720 \| 0.3009 \| - \|
	\| 1.0835 \| 740 \| 0.2928 \| - \|
	\| 1.1127 \| 760 \| 0.2993 \| - \|
	\| 1.1420 \| 780 \| 0.288 \| - \|
	\| 1.1713 \| 800 \| 0.2892 \| - \|
	\| 1.2006 \| 820 \| 0.2934 \| - \|
	\| 1.2299 \| 840 \| 0.2817 \| - \|
	\| 1.2592 \| 860 \| 0.2818 \| - \|
	\| 1.2884 \| 880 \| 0.2857 \| - \|
	\| 1.3177 \| 900 \| 0.2807 \| - \|
	\| 1.3470 \| 920 \| 0.28 \| - \|
	\| 1.3763 \| 940 \| 0.2792 \| - \|
	\| 1.4056 \| 960 \| 0.277 \| - \|
	\| 1.4348 \| 980 \| 0.2783 \| - \|
	\| 1.4641 \| 1000 \| 0.2743 \| - \|
	\| 1.4934 \| 1020 \| 0.2748 \| - \|
	\| 1.5227 \| 1040 \| 0.2731 \| - \|
	\| 1.5520 \| 1060 \| 0.2744 \| - \|
	\| 1.5813 \| 1080 \| 0.2643 \| - \|
	\| 1.6105 \| 1100 \| 0.2742 \| - \|
	\| 1.6398 \| 1120 \| 0.2698 \| - \|
	\| 1.6691 \| 1140 \| 0.2681 \| - \|
	\| 1.6984 \| 1160 \| 0.2698 \| - \|
	\| 1.7277 \| 1180 \| 0.27 \| - \|
	\| 1.7570 \| 1200 \| 0.2642 \| - \|
	\| 1.7862 \| 1220 \| 0.2668 \| - \|
	\| 1.8155 \| 1240 \| 0.2641 \| - \|
	\| 1.8448 \| 1260 \| 0.2645 \| - \|
	\| 1.8741 \| 1280 \| 0.2642 \| - \|
	\| 1.9034 \| 1300 \| 0.2625 \| - \|
	\| 1.9327 \| 1320 \| 0.265 \| - \|
	\| 1.9619 \| 1340 \| 0.2619 \| - \|
	\| 1.9912 \| 1360 \| 0.2643 \| - \|
	\| 2.0 \| 1366 \| - \| 0.2608 \|
	\| 2.0205 \| 1380 \| 0.2661 \| - \|
	\| 2.0498 \| 1400 \| 0.2638 \| - \|
	\| 2.0791 \| 1420 \| 0.2637 \| - \|
	\| 2.1083 \| 1440 \| 0.2597 \| - \|
	\| 2.1376 \| 1460 \| 0.2639 \| - \|
	\| 2.1669 \| 1480 \| 0.2637 \| - \|
	\| 2.1962 \| 1500 \| 0.262 \| - \|
	\| 2.2255 \| 1520 \| 0.2595 \| - \|
	\| 2.2548 \| 1540 \| 0.2564 \| - \|
	\| 2.2840 \| 1560 \| 0.2618 \| - \|
	\| 2.3133 \| 1580 \| 0.2601 \| - \|
	\| 2.3426 \| 1600 \| 0.2585 \| - \|
	\| 2.3719 \| 1620 \| 0.2598 \| - \|
	\| 2.4012 \| 1640 \| 0.2614 \| - \|
	\| 2.4305 \| 1660 \| 0.2543 \| - \|
	\| 2.4597 \| 1680 \| 0.2595 \| - \|
	\| 2.4890 \| 1700 \| 0.2552 \| - \|
	\| 2.5183 \| 1720 \| 0.2565 \| - \|
	\| 2.5476 \| 1740 \| 0.2569 \| - \|
	\| 2.5769 \| 1760 \| 0.2605 \| - \|
	\| 2.6061 \| 1780 \| 0.2581 \| - \|
	\| 2.6354 \| 1800 \| 0.2579 \| - \|
	\| 2.6647 \| 1820 \| 0.2567 \| - \|
	\| 2.6940 \| 1840 \| 0.2516 \| - \|
	\| 2.7233 \| 1860 \| 0.2536 \| - \|
	\| 2.7526 \| 1880 \| 0.2545 \| - \|
	\| 2.7818 \| 1900 \| 0.2548 \| - \|
	\| 2.8111 \| 1920 \| 0.2585 \| - \|
	\| 2.8404 \| 1940 \| 0.2547 \| - \|
	\| 2.8697 \| 1960 \| 0.2495 \| - \|
	\| 2.8990 \| 1980 \| 0.2519 \| - \|
	\| 2.9283 \| 2000 \| 0.2547 \| - \|
	\| 2.9575 \| 2020 \| 0.2561 \| - \|
	\| 2.9868 \| 2040 \| 0.2535 \| - \|
	\| 3.0 \| 2049 \| - \| 0.2526 \|
	\| 3.0161 \| 2060 \| 0.2554 \| - \|
	\| 3.0454 \| 2080 \| 0.2495 \| - \|
	\| 3.0747 \| 2100 \| 0.2537 \| - \|
	\| 3.1040 \| 2120 \| 0.2513 \| - \|
	\| 3.1332 \| 2140 \| 0.2548 \| - \|
	\| 3.1625 \| 2160 \| 0.2562 \| - \|
	\| 3.1918 \| 2180 \| 0.258 \| - \|
	\| 3.2211 \| 2200 \| 0.2547 \| - \|
	\| 3.2504 \| 2220 \| 0.2521 \| - \|
	\| 3.2796 \| 2240 \| 0.2531 \| - \|
	\| 3.3089 \| 2260 \| 0.2532 \| - \|
	\| 3.3382 \| 2280 \| 0.2502 \| - \|
	\| 3.3675 \| 2300 \| 0.2486 \| - \|
	\| 3.3968 \| 2320 \| 0.2498 \| - \|
	\| 3.4261 \| 2340 \| 0.2529 \| - \|
	\| 3.4553 \| 2360 \| 0.2529 \| - \|
	\| 3.4846 \| 2380 \| 0.2469 \| - \|
	\| 3.5139 \| 2400 \| 0.2517 \| - \|
	\| 3.5432 \| 2420 \| 0.2506 \| - \|
	\| 3.5725 \| 2440 \| 0.2468 \| - \|
	\| 3.6018 \| 2460 \| 0.2517 \| - \|
	\| 3.6310 \| 2480 \| 0.2491 \| - \|
	\| 3.6603 \| 2500 \| 0.251 \| - \|
	\| 3.6896 \| 2520 \| 0.2547 \| - \|
	\| 3.7189 \| 2540 \| 0.2488 \| - \|
	\| 3.7482 \| 2560 \| 0.2492 \| - \|
	\| 3.7775 \| 2580 \| 0.2498 \| - \|
	\| 3.8067 \| 2600 \| 0.2521 \| - \|
	\| 3.8360 \| 2620 \| 0.2473 \| - \|
	\| 3.8653 \| 2640 \| 0.2504 \| - \|
	\| 3.8946 \| 2660 \| 0.2466 \| - \|
	\| 3.9239 \| 2680 \| 0.2486 \| - \|
	\| 3.9531 \| 2700 \| 0.249 \| - \|
	\| 3.9824 \| 2720 \| 0.2485 \| - \|
	\| 4.0 \| 2732 \| - \| 0.2477 \|
	\| 4.0117 \| 2740 \| 0.2494 \| - \|
	\| 4.0410 \| 2760 \| 0.2496 \| - \|
	\| 4.0703 \| 2780 \| 0.2487 \| - \|
	\| 4.0996 \| 2800 \| 0.2484 \| - \|
	\| 4.1288 \| 2820 \| 0.2453 \| - \|
	\| 4.1581 \| 2840 \| 0.2444 \| - \|
	\| 4.1874 \| 2860 \| 0.2486 \| - \|
	\| 4.2167 \| 2880 \| 0.2482 \| - \|
	\| 4.2460 \| 2900 \| 0.2491 \| - \|
	\| 4.2753 \| 2920 \| 0.2483 \| - \|
	\| 4.3045 \| 2940 \| 0.2498 \| - \|
	\| 4.3338 \| 2960 \| 0.2462 \| - \|
	\| 4.3631 \| 2980 \| 0.2451 \| - \|
	\| 4.3924 \| 3000 \| 0.2511 \| - \|
	\| 4.4217 \| 3020 \| 0.2464 \| - \|
	\| 4.4510 \| 3040 \| 0.2452 \| - \|
	\| 4.4802 \| 3060 \| 0.2472 \| - \|
	\| 4.5095 \| 3080 \| 0.2474 \| - \|
	\| 4.5388 \| 3100 \| 0.2482 \| - \|
	\| 4.5681 \| 3120 \| 0.2468 \| - \|
	\| 4.5974 \| 3140 \| 0.2511 \| - \|
	\| 4.6266 \| 3160 \| 0.2499 \| - \|
	\| 4.6559 \| 3180 \| 0.2498 \| - \|
	\| 4.6852 \| 3200 \| 0.2476 \| - \|
	\| 4.7145 \| 3220 \| 0.2471 \| - \|
	\| 4.7438 \| 3240 \| 0.2472 \| - \|
	\| 4.7731 \| 3260 \| 0.2464 \| - \|
	\| 4.8023 \| 3280 \| 0.245 \| - \|
	\| 4.8316 \| 3300 \| 0.2475 \| - \|
	\| 4.8609 \| 3320 \| 0.2473 \| - \|
	\| 4.8902 \| 3340 \| 0.2446 \| - \|
	\| 4.9195 \| 3360 \| 0.2436 \| - \|
	\| 4.9488 \| 3380 \| 0.2478 \| - \|
	\| 4.9780 \| 3400 \| 0.2453 \| - \|
	\| 5.0 \| 3415 \| - \| 0.2459 \|
	\| 0.0015 \| 1 \| 0.2562 \| - \|
	\| 0.0293 \| 20 \| 0.2496 \| - \|
	\| 0.0586 \| 40 \| 0.2473 \| - \|
	\| 0.0878 \| 60 \| 0.2431 \| - \|
	\| 0.1171 \| 80 \| 0.2425 \| - \|
	\| 0.1464 \| 100 \| 0.2458 \| - \|
	\| 0.1757 \| 120 \| 0.2435 \| - \|
	\| 0.2050 \| 140 \| 0.2381 \| - \|
	\| 0.2343 \| 160 \| 0.2391 \| - \|
	\| 0.2635 \| 180 \| 0.2353 \| - \|
	\| 0.2928 \| 200 \| 0.2353 \| - \|
	\| 0.3221 \| 220 \| 0.2351 \| - \|
	\| 0.3514 \| 240 \| 0.2302 \| - \|
	\| 0.3807 \| 260 \| 0.2299 \| - \|
	\| 0.4100 \| 280 \| 0.2227 \| - \|
	\| 0.4392 \| 300 \| 0.2264 \| - \|
	\| 0.4685 \| 320 \| 0.2243 \| - \|
	\| 0.4978 \| 340 \| 0.2247 \| - \|
	\| 0.5271 \| 360 \| 0.2195 \| - \|
	\| 0.5564 \| 380 \| 0.2177 \| - \|
	\| 0.5857 \| 400 \| 0.2127 \| - \|
	\| 0.6149 \| 420 \| 0.2164 \| - \|
	\| 0.6442 \| 440 \| 0.2152 \| - \|
	\| 0.6735 \| 460 \| 0.2102 \| - \|
	\| 0.7028 \| 480 \| 0.2102 \| - \|
	\| 0.7321 \| 500 \| 0.2104 \| - \|
	\| 0.7613 \| 520 \| 0.2104 \| - \|
	\| 0.7906 \| 540 \| 0.2121 \| - \|
	\| 0.8199 \| 560 \| 0.2068 \| - \|
	\| 0.8492 \| 580 \| 0.2039 \| - \|
	\| 0.8785 \| 600 \| 0.1995 \| - \|
	\| 0.9078 \| 620 \| 0.2029 \| - \|
	\| 0.9370 \| 640 \| 0.2051 \| - \|
	\| 0.9663 \| 660 \| 0.2049 \| - \|
	\| 0.9956 \| 680 \| 0.2062 \| - \|
	\| 1.0 \| 683 \| - \| 0.2034 \|
	\| 0.0015 \| 1 \| 0.2147 \| - \|
	\| 0.0293 \| 20 \| 0.2061 \| - \|
	\| 0.0586 \| 40 \| 0.2027 \| - \|
	\| 0.0878 \| 60 \| 0.1997 \| - \|
	\| 0.1171 \| 80 \| 0.1948 \| - \|
	\| 0.1464 \| 100 \| 0.1966 \| - \|
	\| 0.1757 \| 120 \| 0.1945 \| - \|
	\| 0.2050 \| 140 \| 0.1834 \| - \|
	\| 0.2343 \| 160 \| 0.1838 \| - \|
	\| 0.2635 \| 180 \| 0.1796 \| - \|
	\| 0.2928 \| 200 \| 0.1761 \| - \|
	\| 0.3221 \| 220 \| 0.1754 \| - \|
	\| 0.3514 \| 240 \| 0.1715 \| - \|
	\| 0.3807 \| 260 \| 0.1691 \| - \|
	\| 0.4100 \| 280 \| 0.1635 \| - \|
	\| 0.4392 \| 300 \| 0.1667 \| - \|
	\| 0.4685 \| 320 \| 0.164 \| - \|
	\| 0.4978 \| 340 \| 0.1639 \| - \|
	\| 0.5271 \| 360 \| 0.1522 \| - \|
	\| 0.5564 \| 380 \| 0.1515 \| - \|
	\| 0.5857 \| 400 \| 0.1535 \| - \|
	\| 0.6149 \| 420 \| 0.1534 \| - \|
	\| 0.6442 \| 440 \| 0.1546 \| - \|
	\| 0.6735 \| 460 \| 0.1523 \| - \|
	\| 0.7028 \| 480 \| 0.1477 \| - \|
	\| 0.7321 \| 500 \| 0.1504 \| - \|
	\| 0.7613 \| 520 \| 0.1485 \| - \|
	\| 0.7906 \| 540 \| 0.1521 \| - \|
	\| 0.8199 \| 560 \| 0.147 \| - \|
	\| 0.8492 \| 580 \| 0.1425 \| - \|
	\| 0.8785 \| 600 \| 0.138 \| - \|
	\| 0.9078 \| 620 \| 0.1414 \| - \|
	\| 0.9370 \| 640 \| 0.1462 \| - \|
	\| 0.9663 \| 660 \| 0.1435 \| - \|
	\| 0.9956 \| 680 \| 0.1462 \| - \|
	\| 1.0 \| 683 \| - \| 0.1555 \|
	\| 0.0015 \| 1 \| 0.1516 \| - \|
	\| 0.0293 \| 20 \| 0.1424 \| - \|
	\| 0.0586 \| 40 \| 0.1432 \| - \|
	\| 0.0878 \| 60 \| 0.1412 \| - \|
	\| 0.1171 \| 80 \| 0.1377 \| - \|
	\| 0.1464 \| 100 \| 0.1396 \| - \|
	\| 0.1757 \| 120 \| 0.1384 \| - \|
	\| 0.2050 \| 140 \| 0.1298 \| - \|
	\| 0.2343 \| 160 \| 0.13 \| - \|
	\| 0.2635 \| 180 \| 0.1312 \| - \|
	\| 0.2928 \| 200 \| 0.1277 \| - \|
	\| 0.3221 \| 220 \| 0.1277 \| - \|
	\| 0.3514 \| 240 \| 0.1278 \| - \|
	\| 0.3807 \| 260 \| 0.1269 \| - \|
	\| 0.4100 \| 280 \| 0.1228 \| - \|
	\| 0.4392 \| 300 \| 0.1257 \| - \|
	\| 0.4685 \| 320 \| 0.1253 \| - \|
	\| 0.4978 \| 340 \| 0.1238 \| - \|
	\| 0.5271 \| 360 \| 0.1121 \| - \|
	\| 0.5564 \| 380 \| 0.1131 \| - \|
	\| 0.5857 \| 400 \| 0.1184 \| - \|
	\| 0.6149 \| 420 \| 0.1176 \| - \|
	\| 0.6442 \| 440 \| 0.1183 \| - \|
	\| 0.6735 \| 460 \| 0.1189 \| - \|
	\| 0.7028 \| 480 \| 0.1136 \| - \|
	\| 0.7321 \| 500 \| 0.1177 \| - \|
	\| 0.7613 \| 520 \| 0.1145 \| - \|
	\| 0.7906 \| 540 \| 0.1175 \| - \|
	\| 0.8199 \| 560 \| 0.1162 \| - \|
	\| 0.8492 \| 580 \| 0.1101 \| - \|
	\| 0.8785 \| 600 \| 0.1065 \| - \|
	\| 0.9078 \| 620 \| 0.1098 \| - \|
	\| 0.9370 \| 640 \| 0.1136 \| - \|
	\| 0.9663 \| 660 \| 0.1119 \| - \|
	\| 0.9956 \| 680 \| 0.1161 \| - \|
	\| 1.0 \| 683 \| - \| 0.1434 \|
	\| 0.0015 \| 1 \| 0.1178 \| - \|
	\| 0.0293 \| 20 \| 0.1088 \| - \|
	\| 0.0586 \| 40 \| 0.112 \| - \|
	\| 0.0878 \| 60 \| 0.1098 \| - \|
	\| 0.1171 \| 80 \| 0.1086 \| - \|
	\| 0.1464 \| 100 \| 0.1097 \| - \|
	\| 0.1757 \| 120 \| 0.1099 \| - \|
	\| 0.2050 \| 140 \| 0.1034 \| - \|
	\| 0.2343 \| 160 \| 0.1047 \| - \|
	\| 0.2635 \| 180 \| 0.1067 \| - \|
	\| 0.2928 \| 200 \| 0.1037 \| - \|
	\| 0.3221 \| 220 \| 0.1031 \| - \|
	\| 0.3514 \| 240 \| 0.1061 \| - \|
	\| 0.3807 \| 260 \| 0.1047 \| - \|
	\| 0.4100 \| 280 \| 0.1024 \| - \|
	\| 0.4392 \| 300 \| 0.1039 \| - \|
	\| 0.4685 \| 320 \| 0.1057 \| - \|
	\| 0.4978 \| 340 \| 0.1031 \| - \|
	\| 0.5271 \| 360 \| 0.0931 \| - \|
	\| 0.5564 \| 380 \| 0.0948 \| - \|
	\| 0.5857 \| 400 \| 0.1006 \| - \|
	\| 0.6149 \| 420 \| 0.1003 \| - \|
	\| 0.6442 \| 440 \| 0.1004 \| - \|
	\| 0.6735 \| 460 \| 0.1018 \| - \|
	\| 0.7028 \| 480 \| 0.0976 \| - \|
	\| 0.7321 \| 500 \| 0.1017 \| - \|
	\| 0.7613 \| 520 \| 0.0981 \| - \|
	\| 0.7906 \| 540 \| 0.1011 \| - \|
	\| 0.8199 \| 560 \| 0.1006 \| - \|
	\| 0.8492 \| 580 \| 0.0949 \| - \|
	\| 0.8785 \| 600 \| 0.092 \| - \|
	\| 0.9078 \| 620 \| 0.095 \| - \|
	\| 0.9370 \| 640 \| 0.0982 \| - \|
	\| 0.9663 \| 660 \| 0.0974 \| - \|
	\| 0.9956 \| 680 \| 0.1023 \| - \|
	\| 1.0 \| 683 \| - \| 0.1398 \|
	\| 0.0015 \| 1 \| 0.1006 \| - \|
	\| 0.0293 \| 20 \| 0.0933 \| - \|
	\| 0.0586 \| 40 \| 0.0973 \| - \|
	\| 0.0878 \| 60 \| 0.0947 \| - \|
	\| 0.1171 \| 80 \| 0.0942 \| - \|
	\| 0.1464 \| 100 \| 0.0945 \| - \|
	\| 0.1757 \| 120 \| 0.0949 \| - \|
	\| 0.2050 \| 140 \| 0.089 \| - \|
	\| 0.2343 \| 160 \| 0.091 \| - \|
	\| 0.2635 \| 180 \| 0.092 \| - \|
	\| 0.2928 \| 200 \| 0.0893 \| - \|
	\| 0.3221 \| 220 \| 0.0883 \| - \|
	\| 0.3514 \| 240 \| 0.0921 \| - \|
	\| 0.3807 \| 260 \| 0.0899 \| - \|
	\| 0.4100 \| 280 \| 0.0887 \| - \|
	\| 0.4392 \| 300 \| 0.0884 \| - \|
	\| 0.4685 \| 320 \| 0.0913 \| - \|
	\| 0.4978 \| 340 \| 0.0881 \| - \|
	\| 0.5271 \| 360 \| 0.0789 \| - \|
	\| 0.5564 \| 380 \| 0.0809 \| - \|
	\| 0.5857 \| 400 \| 0.0864 \| - \|
	\| 0.6149 \| 420 \| 0.0864 \| - \|
	\| 0.6442 \| 440 \| 0.0855 \| - \|
	\| 0.6735 \| 460 \| 0.0869 \| - \|
	\| 0.7028 \| 480 \| 0.0836 \| - \|
	\| 0.7321 \| 500 \| 0.0874 \| - \|
	\| 0.7613 \| 520 \| 0.0834 \| - \|
	\| 0.7906 \| 540 \| 0.086 \| - \|
	\| 0.8199 \| 560 \| 0.0859 \| - \|
	\| 0.8492 \| 580 \| 0.0804 \| - \|
	\| 0.8785 \| 600 \| 0.0786 \| - \|
	\| 0.9078 \| 620 \| 0.0809 \| - \|
	\| 0.9370 \| 640 \| 0.0831 \| - \|
	\| 0.9663 \| 660 \| 0.0831 \| - \|
	\| 0.9956 \| 680 \| 0.0883 \| - \|
	\| 1.0 \| 683 \| - \| 0.1367 \|

	### 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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