SetFit

This is a SetFit model that can be used for Text Classification. A MultiTaskHead instance is used for classification.

The model has been trained using an efficient few-shot learning technique that involves:

Fine-tuning a Sentence Transformer with contrastive learning.
Training a classification head with features from the fine-tuned Sentence Transformer.

Model Details

Model Description

Model Type: SetFit
Classification head: a MultiTaskHead instance
Maximum Sequence Length: 128 tokens

Model Sources

Repository: SetFit on GitHub
Paper: Efficient Few-Shot Learning Without Prompts
Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("setfit_model_id")
# Run inference
preds = model("how to run | BlChicken")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	2	11.1286	49

Training Hyperparameters

batch_size: (32, 32)
num_epochs: (10, 10)
max_steps: -1
sampling_strategy: oversampling
num_iterations: 20
body_learning_rate: (2e-05, 1e-05)
head_learning_rate: 0.001
loss: CosineSimilarityLoss
distance_metric: cosine_distance
margin: 0.25
end_to_end: False
use_amp: False
warmup_proportion: 0.1
l2_weight: 0.01
seed: 42
eval_max_steps: -1
load_best_model_at_end: False

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0009	1	0.2817	-
0.0443	50	0.3201	-
0.0887	100	0.2755	-
0.1330	150	0.2357	-
0.1773	200	0.2212	-
0.2216	250	0.2125	-
0.2660	300	0.1982	-
0.3103	350	0.1819	-
0.3546	400	0.1728	-
0.3989	450	0.1686	-
0.4433	500	0.1513	-
0.4876	550	0.1395	-
0.5319	600	0.1312	-
0.5762	650	0.1165	-
0.6206	700	0.1068	-
0.6649	750	0.098	-
0.7092	800	0.0899	-
0.7535	850	0.0767	-
0.7979	900	0.0762	-
0.8422	950	0.0724	-
0.8865	1000	0.0613	-
0.9309	1050	0.0625	-
0.9752	1100	0.0539	-
1.0195	1150	0.0494	-
1.0638	1200	0.046	-
1.1082	1250	0.0387	-
1.1525	1300	0.0414	-
1.1968	1350	0.0324	-
1.2411	1400	0.0284	-
1.2855	1450	0.0338	-
1.3298	1500	0.0277	-
1.3741	1550	0.0261	-
1.4184	1600	0.0238	-
1.4628	1650	0.0241	-
1.5071	1700	0.0217	-
1.5514	1750	0.019	-
1.5957	1800	0.0173	-
1.6401	1850	0.0157	-
1.6844	1900	0.0145	-
1.7287	1950	0.0171	-
1.7730	2000	0.0131	-
1.8174	2050	0.0148	-
1.8617	2100	0.0163	-
1.9060	2150	0.0131	-
1.9504	2200	0.0087	-
1.9947	2250	0.0113	-
2.0390	2300	0.008	-
2.0833	2350	0.0096	-
2.1277	2400	0.0116	-
2.1720	2450	0.0088	-
2.2163	2500	0.0105	-
2.2606	2550	0.0084	-
2.3050	2600	0.0074	-
2.3493	2650	0.0071	-
2.3936	2700	0.0081	-
2.4379	2750	0.0064	-
2.4823	2800	0.0069	-
2.5266	2850	0.0054	-
2.5709	2900	0.0081	-
2.6152	2950	0.0052	-
2.6596	3000	0.008	-
2.7039	3050	0.0073	-
2.7482	3100	0.0066	-
2.7926	3150	0.0054	-
2.8369	3200	0.0066	-
2.8812	3250	0.0048	-
2.9255	3300	0.0063	-
2.9699	3350	0.0045	-
3.0142	3400	0.0056	-
3.0585	3450	0.0031	-
3.1028	3500	0.0042	-
3.1472	3550	0.0029	-
3.1915	3600	0.0048	-
3.2358	3650	0.0044	-
3.2801	3700	0.0034	-
3.3245	3750	0.0041	-
3.3688	3800	0.0046	-
3.4131	3850	0.0031	-
3.4574	3900	0.0033	-
3.5018	3950	0.0041	-
3.5461	4000	0.0038	-
3.5904	4050	0.0028	-
3.6348	4100	0.0032	-
3.6791	4150	0.0035	-
3.7234	4200	0.0025	-
3.7677	4250	0.0049	-
3.8121	4300	0.0033	-
3.8564	4350	0.0028	-
3.9007	4400	0.0029	-
3.9450	4450	0.0027	-
3.9894	4500	0.0032	-
4.0337	4550	0.002	-
4.0780	4600	0.0023	-
4.1223	4650	0.0021	-
4.1667	4700	0.0014	-
4.2110	4750	0.002	-
4.2553	4800	0.0026	-
4.2996	4850	0.002	-
4.3440	4900	0.0025	-
4.3883	4950	0.0019	-
4.4326	5000	0.004	-
4.4770	5050	0.003	-
4.5213	5100	0.0019	-
4.5656	5150	0.0019	-
4.6099	5200	0.0017	-
4.6543	5250	0.0019	-
4.6986	5300	0.0024	-
4.7429	5350	0.0014	-
4.7872	5400	0.002	-
4.8316	5450	0.0023	-
4.8759	5500	0.0019	-
4.9202	5550	0.0035	-
4.9645	5600	0.0021	-
5.0089	5650	0.0024	-
5.0532	5700	0.0015	-
5.0975	5750	0.0017	-
5.1418	5800	0.0022	-
5.1862	5850	0.0015	-
5.2305	5900	0.0014	-
5.2748	5950	0.0014	-
5.3191	6000	0.0016	-
5.3635	6050	0.0017	-
5.4078	6100	0.0018	-
5.4521	6150	0.001	-
5.4965	6200	0.0016	-
5.5408	6250	0.0023	-
5.5851	6300	0.0011	-
5.6294	6350	0.0015	-
5.6738	6400	0.0026	-
5.7181	6450	0.0012	-
5.7624	6500	0.0011	-
5.8067	6550	0.0013	-
5.8511	6600	0.0007	-
5.8954	6650	0.0016	-
5.9397	6700	0.0022	-
5.9840	6750	0.0009	-
6.0284	6800	0.0009	-
6.0727	6850	0.0014	-
6.1170	6900	0.001	-
6.1613	6950	0.001	-
6.2057	7000	0.0007	-
6.25	7050	0.0015	-
6.2943	7100	0.0013	-
6.3387	7150	0.0011	-
6.3830	7200	0.0015	-
6.4273	7250	0.0007	-
6.4716	7300	0.001	-
6.5160	7350	0.0011	-
6.5603	7400	0.0012	-
6.6046	7450	0.0009	-
6.6489	7500	0.0017	-
6.6933	7550	0.0005	-
6.7376	7600	0.0009	-
6.7819	7650	0.0005	-
6.8262	7700	0.0012	-
6.8706	7750	0.0016	-
6.9149	7800	0.0013	-
6.9592	7850	0.001	-
7.0035	7900	0.0009	-
7.0479	7950	0.0011	-
7.0922	8000	0.001	-
7.1365	8050	0.0006	-
7.1809	8100	0.0014	-
7.2252	8150	0.0006	-
7.2695	8200	0.0011	-
7.3138	8250	0.0007	-
7.3582	8300	0.0015	-
7.4025	8350	0.001	-
7.4468	8400	0.0006	-
7.4911	8450	0.0011	-
7.5355	8500	0.0009	-
7.5798	8550	0.0009	-
7.6241	8600	0.001	-
7.6684	8650	0.0011	-
7.7128	8700	0.001	-
7.7571	8750	0.0009	-
7.8014	8800	0.0012	-
7.8457	8850	0.0005	-
7.8901	8900	0.0006	-
7.9344	8950	0.0006	-
7.9787	9000	0.0005	-
8.0230	9050	0.0007	-
8.0674	9100	0.0005	-
8.1117	9150	0.0008	-
8.1560	9200	0.0005	-
8.2004	9250	0.0004	-
8.2447	9300	0.0006	-
8.2890	9350	0.0005	-
8.3333	9400	0.0009	-
8.3777	9450	0.0004	-
8.4220	9500	0.0005	-
8.4663	9550	0.0011	-
8.5106	9600	0.0007	-
8.5550	9650	0.0005	-
8.5993	9700	0.0011	-
8.6436	9750	0.0009	-
8.6879	9800	0.0013	-
8.7323	9850	0.0006	-
8.7766	9900	0.0008	-
8.8209	9950	0.0007	-
8.8652	10000	0.0007	-
8.9096	10050	0.0005	-
8.9539	10100	0.0008	-
8.9982	10150	0.0008	-
9.0426	10200	0.0004	-
9.0869	10250	0.0004	-
9.1312	10300	0.0009	-
9.1755	10350	0.001	-
9.2199	10400	0.0007	-
9.2642	10450	0.0003	-
9.3085	10500	0.0005	-
9.3528	10550	0.0004	-
9.3972	10600	0.0008	-
9.4415	10650	0.0009	-
9.4858	10700	0.0005	-
9.5301	10750	0.0003	-
9.5745	10800	0.0007	-
9.6188	10850	0.0007	-
9.6631	10900	0.0005	-
9.7074	10950	0.0005	-
9.7518	11000	0.0006	-
9.7961	11050	0.0008	-
9.8404	11100	0.0004	-
9.8848	11150	0.0009	-
9.9291	11200	0.0006	-
9.9734	11250	0.0004	-

Framework Versions

Python: 3.10.8
SetFit: 1.1.2
Sentence Transformers: 5.1.0
Transformers: 4.56.0
PyTorch: 2.8.0+cu128
Datasets: 3.6.0
Tokenizers: 0.22.0

Citation

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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Safetensors

Model size

22.7M params

Tensor type

F32

Paper for ThomBors/NLBSE2026-pharo

Efficient Few-Shot Learning Without Prompts

Paper • 2209.11055 • Published Sep 22, 2022 • 4