from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("Hulyyy/req-quality-setfit-hintaware-dict")
# Run inference
preds = model("ok")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	1	13.9330	32

Training Hyperparameters

batch_size: (64, 64)
num_epochs: (2, 2)
max_steps: -1
sampling_strategy: oversampling
num_iterations: 150
body_learning_rate: (1e-05, 1e-05)
head_learning_rate: 1e-05
loss: CosineSimilarityLoss
distance_metric: cosine_distance
margin: 0.25
end_to_end: False
use_amp: True
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
0.0001	1	0.3731	-
0.0032	50	0.3646	-
0.0065	100	0.3619	-
0.0097	150	0.3329	-
0.0130	200	0.3148	-
0.0162	250	0.2878	-
0.0195	300	0.2567	-
0.0227	350	0.2385	-
0.0260	400	0.2273	-
0.0292	450	0.2265	-
0.0325	500	0.2263	-
0.0357	550	0.2279	-
0.0390	600	0.2286	-
0.0422	650	0.2217	-
0.0455	700	0.2225	-
0.0487	750	0.216	-
0.0520	800	0.2213	-
0.0552	850	0.2136	-
0.0585	900	0.2151	-
0.0617	950	0.2084	-
0.0650	1000	0.21	-
0.0682	1050	0.2108	-
0.0715	1100	0.2059	-
0.0747	1150	0.2058	-
0.0780	1200	0.2012	-
0.0812	1250	0.2053	-
0.0845	1300	0.2038	-
0.0877	1350	0.1971	-
0.0910	1400	0.2018	-
0.0942	1450	0.1973	-
0.0975	1500	0.1924	-
0.1007	1550	0.191	-
0.1040	1600	0.1881	-
0.1072	1650	0.1919	-
0.1105	1700	0.1838	-
0.1137	1750	0.1801	-
0.1170	1800	0.1818	-
0.1202	1850	0.1776	-
0.1235	1900	0.1764	-
0.1267	1950	0.1696	-
0.1300	2000	0.1642	-
0.1332	2050	0.1635	-
0.1365	2100	0.1598	-
0.1397	2150	0.1556	-
0.1430	2200	0.1566	-
0.1462	2250	0.1519	-
0.1495	2300	0.146	-
0.1527	2350	0.1498	-
0.1560	2400	0.1401	-
0.1592	2450	0.1459	-
0.1625	2500	0.1346	-
0.1657	2550	0.1392	-
0.1690	2600	0.1337	-
0.1722	2650	0.1306	-
0.1755	2700	0.1256	-
0.1787	2750	0.1279	-
0.1820	2800	0.1291	-
0.1852	2850	0.1205	-
0.1885	2900	0.1169	-
0.1917	2950	0.1164	-
0.1950	3000	0.1089	-
0.1982	3050	0.1088	-
0.2015	3100	0.1096	-
0.2047	3150	0.1092	-
0.2080	3200	0.1064	-
0.2112	3250	0.1013	-
0.2145	3300	0.0937	-
0.2177	3350	0.0928	-
0.2210	3400	0.096	-
0.2242	3450	0.0893	-
0.2275	3500	0.0893	-
0.2307	3550	0.0829	-
0.2340	3600	0.0826	-
0.2372	3650	0.086	-
0.2405	3700	0.0805	-
0.2437	3750	0.073	-
0.2470	3800	0.0763	-
0.2502	3850	0.0709	-
0.2535	3900	0.0774	-
0.2567	3950	0.0706	-
0.2600	4000	0.0661	-
0.2632	4050	0.0614	-
0.2665	4100	0.0605	-
0.2697	4150	0.0638	-
0.2730	4200	0.062	-
0.2762	4250	0.06	-
0.2795	4300	0.0545	-
0.2827	4350	0.0531	-
0.2860	4400	0.0547	-
0.2892	4450	0.048	-
0.2925	4500	0.0542	-
0.2957	4550	0.0487	-
0.2990	4600	0.0508	-
0.3022	4650	0.0553	-
0.3055	4700	0.0554	-
0.3087	4750	0.0478	-
0.3120	4800	0.0462	-
0.3152	4850	0.0436	-
0.3185	4900	0.0468	-
0.3217	4950	0.0475	-
0.3250	5000	0.0429	-
0.3282	5050	0.0405	-
0.3315	5100	0.0457	-
0.3347	5150	0.0381	-
0.3380	5200	0.0431	-
0.3412	5250	0.0407	-
0.3445	5300	0.0406	-
0.3477	5350	0.0389	-
0.3510	5400	0.0373	-
0.3542	5450	0.0382	-
0.3575	5500	0.0359	-
0.3607	5550	0.0336	-
0.3640	5600	0.0369	-
0.3672	5650	0.0362	-
0.3705	5700	0.0354	-
0.3737	5750	0.0357	-
0.3770	5800	0.0318	-
0.3802	5850	0.0343	-
0.3835	5900	0.0314	-
0.3867	5950	0.0271	-
0.3900	6000	0.0282	-
0.3932	6050	0.0315	-
0.3965	6100	0.0291	-
0.3997	6150	0.0305	-
0.4030	6200	0.0283	-
0.4062	6250	0.0301	-
0.4095	6300	0.0264	-
0.4127	6350	0.0327	-
0.4160	6400	0.0264	-
0.4192	6450	0.0273	-
0.4225	6500	0.027	-
0.4257	6550	0.0281	-
0.4290	6600	0.0271	-
0.4322	6650	0.0262	-
0.4355	6700	0.0263	-
0.4387	6750	0.0292	-
0.4420	6800	0.0266	-
0.4452	6850	0.0267	-
0.4485	6900	0.0255	-
0.4517	6950	0.0194	-
0.4550	7000	0.0232	-
0.4582	7050	0.023	-
0.4615	7100	0.0228	-
0.4647	7150	0.0255	-
0.4680	7200	0.0196	-
0.4712	7250	0.0238	-
0.4745	7300	0.0233	-
0.4777	7350	0.0219	-
0.4810	7400	0.0234	-
0.4842	7450	0.0247	-
0.4875	7500	0.0252	-
0.4907	7550	0.0198	-
0.4940	7600	0.0214	-
0.4972	7650	0.0222	-
0.5005	7700	0.0222	-
0.5037	7750	0.0214	-
0.5070	7800	0.0242	-
0.5102	7850	0.0224	-
0.5135	7900	0.0226	-
0.5167	7950	0.0208	-
0.5200	8000	0.0191	-
0.5232	8050	0.0187	-
0.5265	8100	0.0212	-
0.5297	8150	0.0207	-
0.5330	8200	0.0202	-
0.5362	8250	0.0192	-
0.5395	8300	0.0201	-
0.5427	8350	0.0183	-
0.5460	8400	0.0182	-
0.5492	8450	0.0196	-
0.5525	8500	0.0177	-
0.5557	8550	0.0173	-
0.5590	8600	0.0217	-
0.5622	8650	0.0182	-
0.5655	8700	0.0222	-
0.5687	8750	0.0194	-
0.5720	8800	0.018	-
0.5752	8850	0.0156	-
0.5785	8900	0.022	-
0.5817	8950	0.0196	-
0.5850	9000	0.0185	-
0.5882	9050	0.0221	-
0.5915	9100	0.0185	-
0.5947	9150	0.0181	-
0.5980	9200	0.0176	-
0.6012	9250	0.0203	-
0.6045	9300	0.0191	-
0.6077	9350	0.0184	-
0.6110	9400	0.0207	-
0.6142	9450	0.0172	-
0.6175	9500	0.0165	-
0.6207	9550	0.0147	-
0.6240	9600	0.0187	-
0.6272	9650	0.0141	-
0.6305	9700	0.0174	-
0.6337	9750	0.0164	-
0.6370	9800	0.0182	-
0.6402	9850	0.0181	-
0.6435	9900	0.0159	-
0.6467	9950	0.0153	-
0.6500	10000	0.0154	-
0.6532	10050	0.0152	-
0.6565	10100	0.0139	-
0.6597	10150	0.0171	-
0.6630	10200	0.0153	-
0.6662	10250	0.0129	-
0.6695	10300	0.0167	-
0.6727	10350	0.0174	-
0.6760	10400	0.0158	-
0.6792	10450	0.0156	-
0.6825	10500	0.0155	-
0.6857	10550	0.0175	-
0.6890	10600	0.0159	-
0.6922	10650	0.0166	-
0.6955	10700	0.0149	-
0.6987	10750	0.0138	-
0.7020	10800	0.015	-
0.7052	10850	0.0136	-
0.7085	10900	0.0155	-
0.7117	10950	0.0149	-
0.7150	11000	0.0137	-
0.7182	11050	0.0136	-
0.7215	11100	0.0167	-
0.7247	11150	0.0156	-
0.7280	11200	0.0153	-
0.7312	11250	0.0148	-
0.7345	11300	0.0155	-
0.7377	11350	0.0156	-
0.7410	11400	0.0144	-
0.7442	11450	0.0152	-
0.7475	11500	0.0134	-
0.7507	11550	0.0141	-
0.7540	11600	0.0135	-
0.7572	11650	0.0159	-
0.7605	11700	0.0167	-
0.7637	11750	0.0142	-
0.7670	11800	0.0137	-
0.7702	11850	0.0144	-
0.7735	11900	0.0153	-
0.7767	11950	0.0144	-
0.7800	12000	0.0151	-
0.7832	12050	0.0128	-
0.7865	12100	0.0128	-
0.7897	12150	0.0144	-
0.7930	12200	0.0154	-
0.7962	12250	0.015	-
0.7995	12300	0.0127	-
0.8027	12350	0.0153	-
0.8060	12400	0.0114	-
0.8092	12450	0.0146	-
0.8125	12500	0.0136	-
0.8157	12550	0.0124	-
0.8190	12600	0.0143	-
0.8222	12650	0.0126	-
0.8255	12700	0.0152	-
0.8287	12750	0.0124	-
0.8320	12800	0.0125	-
0.8352	12850	0.0126	-
0.8385	12900	0.0147	-
0.8417	12950	0.0135	-
0.8450	13000	0.013	-
0.8482	13050	0.0126	-
0.8515	13100	0.0106	-
0.8547	13150	0.0122	-
0.8580	13200	0.0105	-
0.8612	13250	0.0115	-
0.8645	13300	0.0123	-
0.8677	13350	0.0128	-
0.8710	13400	0.0108	-
0.8742	13450	0.0165	-
0.8775	13500	0.0118	-
0.8807	13550	0.0159	-
0.8840	13600	0.0131	-
0.8872	13650	0.0144	-
0.8905	13700	0.0134	-
0.8937	13750	0.0135	-
0.8970	13800	0.0118	-
0.9002	13850	0.0109	-
0.9035	13900	0.0107	-
0.9067	13950	0.0114	-
0.9100	14000	0.0129	-
0.9132	14050	0.0127	-
0.9165	14100	0.0125	-
0.9197	14150	0.0137	-
0.9230	14200	0.0123	-
0.9262	14250	0.0126	-
0.9295	14300	0.0119	-
0.9327	14350	0.0108	-
0.9360	14400	0.011	-
0.9392	14450	0.0127	-
0.9425	14500	0.0119	-
0.9457	14550	0.0117	-
0.9490	14600	0.0093	-
0.9522	14650	0.0112	-
0.9555	14700	0.0136	-
0.9587	14750	0.0095	-
0.9620	14800	0.011	-
0.9652	14850	0.0115	-
0.9685	14900	0.0098	-
0.9717	14950	0.0123	-
0.9750	15000	0.0135	-
0.9782	15050	0.0101	-
0.9815	15100	0.0116	-
0.9847	15150	0.012	-
0.9880	15200	0.0107	-
0.9912	15250	0.0102	-
0.9945	15300	0.0105	-
0.9977	15350	0.0103	-
1.0010	15400	0.0121	-
1.0042	15450	0.009	-
1.0075	15500	0.0116	-
1.0107	15550	0.0105	-
1.0140	15600	0.0095	-
1.0172	15650	0.0114	-
1.0205	15700	0.0114	-
1.0237	15750	0.0107	-
1.0270	15800	0.0125	-
1.0302	15850	0.0093	-
1.0335	15900	0.0099	-
1.0367	15950	0.013	-
1.0400	16000	0.0095	-
1.0432	16050	0.0092	-
1.0465	16100	0.0121	-
1.0497	16150	0.0107	-
1.0530	16200	0.0106	-
1.0562	16250	0.0103	-
1.0595	16300	0.0102	-
1.0627	16350	0.0111	-
1.0660	16400	0.0089	-
1.0692	16450	0.0113	-
1.0725	16500	0.0101	-
1.0757	16550	0.0098	-
1.0790	16600	0.0098	-
1.0822	16650	0.0098	-
1.0855	16700	0.0088	-
1.0887	16750	0.0089	-
1.0920	16800	0.0116	-
1.0952	16850	0.0081	-
1.0985	16900	0.0097	-
1.1017	16950	0.0091	-
1.1050	17000	0.0098	-
1.1082	17050	0.01	-
1.1115	17100	0.0105	-
1.1147	17150	0.0113	-
1.1180	17200	0.0102	-
1.1212	17250	0.0116	-
1.1245	17300	0.0106	-
1.1277	17350	0.011	-
1.1310	17400	0.0122	-
1.1342	17450	0.0098	-
1.1375	17500	0.0087	-
1.1407	17550	0.0107	-
1.1440	17600	0.0096	-
1.1472	17650	0.0093	-
1.1505	17700	0.0093	-
1.1537	17750	0.0099	-
1.1570	17800	0.0112	-
1.1602	17850	0.0086	-
1.1635	17900	0.0105	-
1.1667	17950	0.0095	-
1.1700	18000	0.0107	-
1.1732	18050	0.0089	-
1.1765	18100	0.0112	-
1.1797	18150	0.0092	-
1.1830	18200	0.0104	-
1.1862	18250	0.011	-
1.1895	18300	0.0117	-
1.1927	18350	0.0104	-
1.1960	18400	0.0093	-
1.1992	18450	0.0093	-
1.2025	18500	0.0088	-
1.2057	18550	0.009	-
1.2090	18600	0.0087	-
1.2122	18650	0.0099	-
1.2155	18700	0.0097	-
1.2187	18750	0.0093	-
1.2220	18800	0.0102	-
1.2252	18850	0.0106	-
1.2285	18900	0.01	-
1.2317	18950	0.009	-
1.2350	19000	0.0095	-
1.2382	19050	0.0107	-
1.2415	19100	0.0091	-
1.2447	19150	0.0116	-
1.2480	19200	0.0089	-
1.2512	19250	0.0104	-
1.2545	19300	0.0084	-
1.2577	19350	0.0089	-
1.2610	19400	0.0121	-
1.2642	19450	0.0108	-
1.2675	19500	0.0103	-
1.2707	19550	0.0097	-
1.2740	19600	0.0107	-
1.2772	19650	0.0103	-
1.2805	19700	0.0097	-
1.2837	19750	0.0099	-
1.2870	19800	0.0112	-
1.2902	19850	0.0088	-
1.2935	19900	0.0086	-
1.2967	19950	0.0092	-
1.3000	20000	0.0084	-
1.3032	20050	0.0093	-
1.3065	20100	0.0098	-
1.3097	20150	0.008	-
1.3130	20200	0.0084	-
1.3162	20250	0.0085	-
1.3195	20300	0.0101	-
1.3227	20350	0.0092	-
1.3260	20400	0.01	-
1.3292	20450	0.0101	-
1.3325	20500	0.0101	-
1.3357	20550	0.0076	-
1.3390	20600	0.009	-
1.3422	20650	0.0108	-
1.3455	20700	0.0087	-
1.3487	20750	0.008	-
1.3520	20800	0.0097	-
1.3552	20850	0.0087	-
1.3585	20900	0.0089	-
1.3617	20950	0.009	-
1.3650	21000	0.009	-
1.3682	21050	0.0093	-
1.3715	21100	0.0096	-
1.3747	21150	0.0075	-
1.3780	21200	0.0076	-
1.3812	21250	0.0082	-
1.3845	21300	0.01	-
1.3877	21350	0.0077	-
1.3910	21400	0.0073	-
1.3942	21450	0.0087	-
1.3975	21500	0.0106	-
1.4007	21550	0.0095	-
1.4040	21600	0.0089	-
1.4072	21650	0.0091	-
1.4105	21700	0.0098	-
1.4137	21750	0.0098	-
1.4170	21800	0.0087	-
1.4202	21850	0.0092	-
1.4235	21900	0.0095	-
1.4267	21950	0.0077	-
1.4300	22000	0.0078	-
1.4332	22050	0.0076	-
1.4365	22100	0.0092	-
1.4397	22150	0.0098	-
1.4430	22200	0.0099	-
1.4462	22250	0.0089	-
1.4495	22300	0.011	-
1.4527	22350	0.008	-
1.4560	22400	0.0076	-
1.4592	22450	0.0089	-
1.4625	22500	0.0083	-
1.4657	22550	0.007	-
1.4690	22600	0.0093	-
1.4722	22650	0.0076	-
1.4755	22700	0.0091	-
1.4787	22750	0.0081	-
1.4820	22800	0.0096	-
1.4852	22850	0.0088	-
1.4885	22900	0.008	-
1.4917	22950	0.0082	-
1.4950	23000	0.0074	-
1.4982	23050	0.0091	-
1.5015	23100	0.009	-
1.5047	23150	0.0072	-
1.5080	23200	0.0078	-
1.5112	23250	0.0092	-
1.5145	23300	0.0081	-
1.5177	23350	0.0083	-
1.5210	23400	0.0101	-
1.5242	23450	0.0066	-
1.5275	23500	0.0091	-
1.5307	23550	0.009	-
1.5340	23600	0.0088	-
1.5372	23650	0.0104	-
1.5405	23700	0.0108	-
1.5437	23750	0.0079	-
1.5470	23800	0.0108	-
1.5502	23850	0.0086	-
1.5535	23900	0.0069	-
1.5567	23950	0.008	-
1.5600	24000	0.0068	-
1.5632	24050	0.0076	-
1.5665	24100	0.0094	-
1.5697	24150	0.0064	-
1.5730	24200	0.0099	-
1.5762	24250	0.0082	-
1.5795	24300	0.0077	-
1.5827	24350	0.0085	-
1.5860	24400	0.0076	-
1.5892	24450	0.0089	-
1.5925	24500	0.0076	-
1.5957	24550	0.0091	-
1.5990	24600	0.0081	-
1.6022	24650	0.0088	-
1.6055	24700	0.0085	-
1.6087	24750	0.0078	-
1.6120	24800	0.0079	-
1.6152	24850	0.0084	-
1.6185	24900	0.0074	-
1.6217	24950	0.0069	-
1.6250	25000	0.0088	-
1.6282	25050	0.0081	-
1.6315	25100	0.0095	-
1.6347	25150	0.0091	-
1.6380	25200	0.0069	-
1.6412	25250	0.0094	-
1.6445	25300	0.0069	-
1.6477	25350	0.0086	-
1.6510	25400	0.0093	-
1.6542	25450	0.0085	-
1.6575	25500	0.0086	-
1.6607	25550	0.0076	-
1.6640	25600	0.0089	-
1.6672	25650	0.0083	-
1.6705	25700	0.0092	-
1.6737	25750	0.009	-
1.6770	25800	0.0087	-
1.6802	25850	0.0077	-
1.6835	25900	0.0087	-
1.6867	25950	0.0084	-
1.6900	26000	0.0079	-
1.6932	26050	0.0067	-
1.6965	26100	0.0094	-
1.6997	26150	0.0096	-
1.7030	26200	0.0082	-
1.7062	26250	0.0073	-
1.7095	26300	0.0067	-
1.7127	26350	0.008	-
1.7160	26400	0.0085	-
1.7192	26450	0.0065	-
1.7225	26500	0.0105	-
1.7257	26550	0.0089	-
1.7290	26600	0.0095	-
1.7322	26650	0.0079	-
1.7355	26700	0.0089	-
1.7387	26750	0.0087	-
1.7420	26800	0.0088	-
1.7452	26850	0.0078	-
1.7485	26900	0.0093	-
1.7517	26950	0.0098	-
1.7550	27000	0.0087	-
1.7582	27050	0.0092	-
1.7615	27100	0.0087	-
1.7647	27150	0.0085	-
1.7680	27200	0.0088	-
1.7712	27250	0.0088	-
1.7745	27300	0.0067	-
1.7777	27350	0.0088	-
1.7810	27400	0.008	-
1.7842	27450	0.0077	-
1.7875	27500	0.0079	-
1.7907	27550	0.0076	-
1.7940	27600	0.0075	-
1.7972	27650	0.0078	-
1.8005	27700	0.0065	-
1.8037	27750	0.0066	-
1.8070	27800	0.0084	-
1.8102	27850	0.0084	-
1.8135	27900	0.0108	-
1.8167	27950	0.0068	-
1.8200	28000	0.008	-
1.8232	28050	0.0072	-
1.8265	28100	0.0078	-
1.8297	28150	0.0085	-
1.8330	28200	0.0082	-
1.8362	28250	0.0065	-
1.8395	28300	0.0074	-
1.8427	28350	0.0078	-
1.8460	28400	0.0076	-
1.8492	28450	0.0076	-
1.8525	28500	0.0077	-
1.8557	28550	0.0079	-
1.8590	28600	0.0069	-
1.8622	28650	0.0085	-
1.8655	28700	0.0085	-
1.8687	28750	0.008	-
1.8720	28800	0.0098	-
1.8752	28850	0.0086	-
1.8785	28900	0.0072	-
1.8817	28950	0.0089	-
1.8850	29000	0.0086	-
1.8882	29050	0.008	-
1.8915	29100	0.0084	-
1.8947	29150	0.0057	-
1.8980	29200	0.0091	-
1.9012	29250	0.0064	-
1.9045	29300	0.0094	-
1.9077	29350	0.0069	-
1.9110	29400	0.0074	-
1.9142	29450	0.0068	-
1.9175	29500	0.0076	-
1.9207	29550	0.0093	-
1.9240	29600	0.0077	-
1.9272	29650	0.0076	-
1.9305	29700	0.0094	-
1.9337	29750	0.0076	-
1.9370	29800	0.0095	-
1.9402	29850	0.0071	-
1.9435	29900	0.0082	-
1.9467	29950	0.009	-
1.9500	30000	0.0092	-
1.9532	30050	0.0076	-
1.9565	30100	0.0066	-
1.9597	30150	0.0058	-
1.9630	30200	0.0093	-
1.9662	30250	0.0106	-
1.9695	30300	0.0079	-
1.9727	30350	0.0086	-
1.9760	30400	0.0101	-
1.9792	30450	0.0074	-
1.9825	30500	0.0079	-
1.9857	30550	0.0071	-
1.9890	30600	0.0086	-
1.9922	30650	0.0076	-
1.9955	30700	0.0088	-
1.9987	30750	0.0086	-

Framework Versions

Python: 3.13.7
SetFit: 1.1.3
Sentence Transformers: 5.1.1
Transformers: 4.57.0
PyTorch: 2.8.0+cu129
Datasets: 4.2.0
Tokenizers: 0.22.1

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}
}