ThomBors/NLBSE2026-python

SetFit with sentence-transformers/paraphrase-MiniLM-L6-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-MiniLM-L6-v2 as the Sentence Transformer embedding model. A MultiOutputClassifier 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 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: sentence-transformers/paraphrase-MiniLM-L6-v2
• Classification head: a MultiOutputClassifier 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("table. | PlotAccessor")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	1	8.8783	40

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: 0.01
• 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
0.0001	1	0.3082	-
0.0028	50	0.3776	-
0.0056	100	0.3684	-
0.0084	150	0.3702	-
0.0112	200	0.3521	-
0.0140	250	0.3383	-
0.0168	300	0.3227	-
0.0196	350	0.3141	-
0.0224	400	0.2816	-
0.0251	450	0.2717	-
0.0279	500	0.2663	-
0.0307	550	0.262	-
0.0335	600	0.2619	-
0.0363	650	0.2575	-
0.0391	700	0.253	-
0.0419	750	0.2586	-
0.0447	800	0.2535	-
0.0475	850	0.2557	-
0.0503	900	0.2555	-
0.0531	950	0.2563	-
0.0559	1000	0.2513	-
0.0587	1050	0.2535	-
0.0615	1100	0.2494	-
0.0643	1150	0.251	-
0.0671	1200	0.253	-
0.0698	1250	0.2496	-
0.0726	1300	0.2484	-
0.0754	1350	0.2512	-
0.0782	1400	0.2485	-
0.0810	1450	0.2513	-
0.0838	1500	0.248	-
0.0866	1550	0.2513	-
0.0894	1600	0.2479	-
0.0922	1650	0.2498	-
0.0950	1700	0.2486	-
0.0978	1750	0.2513	-
0.1006	1800	0.2485	-
0.1034	1850	0.25	-
0.1062	1900	0.2465	-
0.1090	1950	0.2453	-
0.1118	2000	0.2436	-
0.1145	2050	0.2455	-
0.1173	2100	0.2433	-
0.1201	2150	0.2469	-
0.1229	2200	0.2455	-
0.1257	2250	0.2485	-
0.1285	2300	0.2442	-
0.1313	2350	0.2459	-
0.1341	2400	0.2466	-
0.1369	2450	0.2477	-
0.1397	2500	0.2483	-
0.1425	2550	0.243	-
0.1453	2600	0.2429	-
0.1481	2650	0.2434	-
0.1509	2700	0.2396	-
0.1537	2750	0.2417	-
0.1565	2800	0.2402	-
0.1592	2850	0.2404	-
0.1620	2900	0.2406	-
0.1648	2950	0.2422	-
0.1676	3000	0.2338	-
0.1704	3050	0.2369	-
0.1732	3100	0.2359	-
0.1760	3150	0.241	-
0.1788	3200	0.2349	-
0.1816	3250	0.2365	-
0.1844	3300	0.2319	-
0.1872	3350	0.2309	-
0.1900	3400	0.2311	-
0.1928	3450	0.2256	-
0.1956	3500	0.2367	-
0.1984	3550	0.2276	-
0.2012	3600	0.2293	-
0.2039	3650	0.2219	-
0.2067	3700	0.2255	-
0.2095	3750	0.2184	-
0.2123	3800	0.2281	-
0.2151	3850	0.2185	-
0.2179	3900	0.2214	-
0.2207	3950	0.2216	-
0.2235	4000	0.215	-
0.2263	4050	0.2134	-
0.2291	4100	0.215	-
0.2319	4150	0.2008	-
0.2347	4200	0.2091	-
0.2375	4250	0.2071	-
0.2403	4300	0.2102	-
0.2431	4350	0.2012	-
0.2459	4400	0.2009	-
0.2486	4450	0.1955	-
0.2514	4500	0.204	-
0.2542	4550	0.1998	-
0.2570	4600	0.1888	-
0.2598	4650	0.1904	-
0.2626	4700	0.1881	-
0.2654	4750	0.1882	-
0.2682	4800	0.1844	-
0.2710	4850	0.1868	-
0.2738	4900	0.1812	-
0.2766	4950	0.1799	-
0.2794	5000	0.1812	-
0.2822	5050	0.176	-
0.2850	5100	0.1835	-
0.2878	5150	0.175	-
0.2906	5200	0.1702	-
0.2933	5250	0.1738	-
0.2961	5300	0.1668	-
0.2989	5350	0.1712	-
0.3017	5400	0.1728	-
0.3045	5450	0.1643	-
0.3073	5500	0.158	-
0.3101	5550	0.167	-
0.3129	5600	0.1653	-
0.3157	5650	0.1617	-
0.3185	5700	0.1618	-
0.3213	5750	0.1633	-
0.3241	5800	0.1599	-
0.3269	5850	0.1598	-
0.3297	5900	0.1532	-
0.3325	5950	0.1481	-
0.3353	6000	0.163	-
0.3380	6050	0.1555	-
0.3408	6100	0.157	-
0.3436	6150	0.1511	-
0.3464	6200	0.1438	-
0.3492	6250	0.1556	-
0.3520	6300	0.1507	-
0.3548	6350	0.1445	-
0.3576	6400	0.1396	-
0.3604	6450	0.1406	-
0.3632	6500	0.1412	-
0.3660	6550	0.142	-
0.3688	6600	0.1372	-
0.3716	6650	0.1365	-
0.3744	6700	0.1396	-
0.3772	6750	0.1297	-
0.3800	6800	0.1309	-
0.3827	6850	0.1422	-
0.3855	6900	0.1335	-
0.3883	6950	0.1315	-
0.3911	7000	0.1288	-
0.3939	7050	0.1311	-
0.3967	7100	0.1245	-
0.3995	7150	0.1311	-
0.4023	7200	0.1251	-
0.4051	7250	0.1289	-
0.4079	7300	0.1292	-
0.4107	7350	0.125	-
0.4135	7400	0.1161	-
0.4163	7450	0.1215	-
0.4191	7500	0.1171	-
0.4219	7550	0.1218	-
0.4247	7600	0.1256	-
0.4274	7650	0.114	-
0.4302	7700	0.1061	-
0.4330	7750	0.1174	-
0.4358	7800	0.1157	-
0.4386	7850	0.1124	-
0.4414	7900	0.1119	-
0.4442	7950	0.1106	-
0.4470	8000	0.1056	-
0.4498	8050	0.1145	-
0.4526	8100	0.1039	-
0.4554	8150	0.1026	-
0.4582	8200	0.1042	-
0.4610	8250	0.1021	-
0.4638	8300	0.1006	-
0.4666	8350	0.1149	-
0.4694	8400	0.1024	-
0.4721	8450	0.1005	-
0.4749	8500	0.0984	-
0.4777	8550	0.1041	-
0.4805	8600	0.0983	-
0.4833	8650	0.0999	-
0.4861	8700	0.101	-
0.4889	8750	0.0999	-
0.4917	8800	0.0909	-
0.4945	8850	0.0971	-
0.4973	8900	0.0907	-
0.5001	8950	0.0989	-
0.5029	9000	0.0888	-
0.5057	9050	0.0957	-
0.5085	9100	0.101	-
0.5113	9150	0.0974	-
0.5141	9200	0.0916	-
0.5168	9250	0.0894	-
0.5196	9300	0.0875	-
0.5224	9350	0.0832	-
0.5252	9400	0.0895	-
0.5280	9450	0.0859	-
0.5308	9500	0.085	-
0.5336	9550	0.0912	-
0.5364	9600	0.0905	-
0.5392	9650	0.0853	-
0.5420	9700	0.0826	-
0.5448	9750	0.0839	-
0.5476	9800	0.0802	-
0.5504	9850	0.0801	-
0.5532	9900	0.084	-
0.5560	9950	0.0867	-
0.5588	10000	0.0815	-
0.5615	10050	0.0804	-
0.5643	10100	0.0803	-
0.5671	10150	0.0767	-
0.5699	10200	0.0772	-
0.5727	10250	0.0783	-
0.5755	10300	0.0742	-
0.5783	10350	0.0726	-
0.5811	10400	0.0753	-
0.5839	10450	0.0744	-
0.5867	10500	0.0767	-
0.5895	10550	0.0717	-
0.5923	10600	0.074	-
0.5951	10650	0.0709	-
0.5979	10700	0.0757	-
0.6007	10750	0.0735	-
0.6035	10800	0.0653	-
0.6062	10850	0.0672	-
0.6090	10900	0.0638	-
0.6118	10950	0.0697	-
0.6146	11000	0.0716	-
0.6174	11050	0.0671	-
0.6202	11100	0.0713	-
0.6230	11150	0.0655	-
0.6258	11200	0.0667	-
0.6286	11250	0.0649	-
0.6314	11300	0.0717	-
0.6342	11350	0.0645	-
0.6370	11400	0.0606	-
0.6398	11450	0.0623	-
0.6426	11500	0.0583	-
0.6454	11550	0.0578	-
0.6482	11600	0.0607	-
0.6509	11650	0.0573	-
0.6537	11700	0.0581	-
0.6565	11750	0.0544	-
0.6593	11800	0.0576	-
0.6621	11850	0.0589	-
0.6649	11900	0.0611	-
0.6677	11950	0.0628	-
0.6705	12000	0.0603	-
0.6733	12050	0.0572	-
0.6761	12100	0.0601	-
0.6789	12150	0.0578	-
0.6817	12200	0.0604	-
0.6845	12250	0.0541	-
0.6873	12300	0.0602	-
0.6901	12350	0.0564	-
0.6929	12400	0.0535	-
0.6956	12450	0.0495	-
0.6984	12500	0.0514	-
0.7012	12550	0.0508	-
0.7040	12600	0.0509	-
0.7068	12650	0.0539	-
0.7096	12700	0.052	-
0.7124	12750	0.0525	-
0.7152	12800	0.0501	-
0.7180	12850	0.0459	-
0.7208	12900	0.0529	-
0.7236	12950	0.0466	-
0.7264	13000	0.0496	-
0.7292	13050	0.0494	-
0.7320	13100	0.0448	-
0.7348	13150	0.049	-
0.7376	13200	0.0437	-
0.7403	13250	0.0453	-
0.7431	13300	0.0494	-
0.7459	13350	0.0464	-
0.7487	13400	0.0423	-
0.7515	13450	0.0441	-
0.7543	13500	0.0483	-
0.7571	13550	0.0412	-
0.7599	13600	0.0482	-
0.7627	13650	0.0356	-
0.7655	13700	0.0438	-
0.7683	13750	0.0441	-
0.7711	13800	0.0407	-
0.7739	13850	0.0421	-
0.7767	13900	0.0417	-
0.7795	13950	0.0389	-
0.7823	14000	0.0353	-
0.7850	14050	0.0416	-
0.7878	14100	0.0436	-
0.7906	14150	0.041	-
0.7934	14200	0.0387	-
0.7962	14250	0.0374	-
0.7990	14300	0.0344	-
0.8018	14350	0.0387	-
0.8046	14400	0.0387	-
0.8074	14450	0.0394	-
0.8102	14500	0.04	-
0.8130	14550	0.0402	-
0.8158	14600	0.0392	-
0.8186	14650	0.0327	-
0.8214	14700	0.0322	-
0.8242	14750	0.0358	-
0.8270	14800	0.0359	-
0.8297	14850	0.0392	-
0.8325	14900	0.0349	-
0.8353	14950	0.0364	-
0.8381	15000	0.0352	-
0.8409	15050	0.0325	-
0.8437	15100	0.0343	-
0.8465	15150	0.0367	-
0.8493	15200	0.0364	-
0.8521	15250	0.0335	-
0.8549	15300	0.0345	-
0.8577	15350	0.0354	-
0.8605	15400	0.0401	-
0.8633	15450	0.0329	-
0.8661	15500	0.0334	-
0.8689	15550	0.0315	-
0.8717	15600	0.0345	-
0.8744	15650	0.034	-
0.8772	15700	0.0341	-
0.8800	15750	0.0295	-
0.8828	15800	0.0303	-
0.8856	15850	0.034	-
0.8884	15900	0.0314	-
0.8912	15950	0.0308	-
0.8940	16000	0.0325	-
0.8968	16050	0.0299	-
0.8996	16100	0.0323	-
0.9024	16150	0.0306	-
0.9052	16200	0.0338	-
0.9080	16250	0.0255	-
0.9108	16300	0.0318	-
0.9136	16350	0.0293	-
0.9164	16400	0.0294	-
0.9191	16450	0.0325	-
0.9219	16500	0.0333	-
0.9247	16550	0.0304	-
0.9275	16600	0.0276	-
0.9303	16650	0.0315	-
0.9331	16700	0.0287	-
0.9359	16750	0.0282	-
0.9387	16800	0.0275	-
0.9415	16850	0.0251	-
0.9443	16900	0.0282	-
0.9471	16950	0.0264	-
0.9499	17000	0.0315	-
0.9527	17050	0.0301	-
0.9555	17100	0.0304	-
0.9583	17150	0.0306	-
0.9611	17200	0.027	-
0.9638	17250	0.0286	-
0.9666	17300	0.0271	-
0.9694	17350	0.0276	-
0.9722	17400	0.0284	-
0.9750	17450	0.0261	-
0.9778	17500	0.025	-
0.9806	17550	0.0276	-
0.9834	17600	0.0278	-
0.9862	17650	0.0271	-
0.9890	17700	0.0253	-
0.9918	17750	0.0249	-
0.9946	17800	0.029	-
0.9974	17850	0.0294	-
1.0002	17900	0.0266	-
1.0030	17950	0.0234	-
1.0058	18000	0.0275	-
1.0085	18050	0.0251	-
1.0113	18100	0.0278	-
1.0141	18150	0.0243	-
1.0169	18200	0.024	-
1.0197	18250	0.0266	-
1.0225	18300	0.0267	-
1.0253	18350	0.0259	-
1.0281	18400	0.0281	-
1.0309	18450	0.0266	-
1.0337	18500	0.0246	-
1.0365	18550	0.0253	-
1.0393	18600	0.0223	-
1.0421	18650	0.0247	-
1.0449	18700	0.0277	-
1.0477	18750	0.0212	-
1.0505	18800	0.0286	-
1.0532	18850	0.0253	-
1.0560	18900	0.023	-
1.0588	18950	0.0254	-
1.0616	19000	0.0236	-
1.0644	19050	0.0255	-
1.0672	19100	0.0231	-
1.0700	19150	0.0242	-
1.0728	19200	0.0249	-
1.0756	19250	0.0244	-
1.0784	19300	0.026	-
1.0812	19350	0.0247	-
1.0840	19400	0.0231	-
1.0868	19450	0.0228	-
1.0896	19500	0.0226	-
1.0924	19550	0.0217	-
1.0952	19600	0.0243	-
1.0979	19650	0.0221	-
1.1007	19700	0.0275	-
1.1035	19750	0.0234	-
1.1063	19800	0.0249	-
1.1091	19850	0.0219	-
1.1119	19900	0.0187	-
1.1147	19950	0.023	-
1.1175	20000	0.0224	-
1.1203	20050	0.0219	-
1.1231	20100	0.0254	-
1.1259	20150	0.0262	-
1.1287	20200	0.0211	-
1.1315	20250	0.0221	-
1.1343	20300	0.0224	-
1.1371	20350	0.0256	-
1.1399	20400	0.0214	-
1.1426	20450	0.0224	-
1.1454	20500	0.0235	-
1.1482	20550	0.0214	-
1.1510	20600	0.0219	-
1.1538	20650	0.0185	-
1.1566	20700	0.0222	-
1.1594	20750	0.0225	-
1.1622	20800	0.0251	-
1.1650	20850	0.0231	-
1.1678	20900	0.0228	-
1.1706	20950	0.0208	-
1.1734	21000	0.0242	-
1.1762	21050	0.0213	-
1.1790	21100	0.0217	-
1.1818	21150	0.0218	-
1.1846	21200	0.021	-
1.1873	21250	0.0187	-
1.1901	21300	0.0212	-
1.1929	21350	0.0219	-
1.1957	21400	0.0229	-
1.1985	21450	0.0225	-
1.2013	21500	0.022	-
1.2041	21550	0.0216	-
1.2069	21600	0.021	-
1.2097	21650	0.0219	-
1.2125	21700	0.0203	-
1.2153	21750	0.0202	-
1.2181	21800	0.0206	-
1.2209	21850	0.0203	-
1.2237	21900	0.0203	-
1.2265	21950	0.0196	-
1.2293	22000	0.0189	-
1.2321	22050	0.0217	-
1.2348	22100	0.0213	-
1.2376	22150	0.0201	-
1.2404	22200	0.0201	-
1.2432	22250	0.0206	-
1.2460	22300	0.023	-
1.2488	22350	0.0201	-
1.2516	22400	0.0202	-
1.2544	22450	0.0214	-
1.2572	22500	0.0183	-
1.2600	22550	0.0187	-
1.2628	22600	0.0214	-
1.2656	22650	0.0198	-
1.2684	22700	0.0221	-
1.2712	22750	0.0189	-
1.2740	22800	0.0206	-
1.2768	22850	0.0205	-
1.2795	22900	0.0221	-
1.2823	22950	0.0196	-
1.2851	23000	0.0211	-
1.2879	23050	0.0208	-
1.2907	23100	0.0228	-
1.2935	23150	0.0191	-
1.2963	23200	0.0197	-
1.2991	23250	0.0213	-
1.3019	23300	0.0212	-
1.3047	23350	0.0199	-
1.3075	23400	0.0221	-
1.3103	23450	0.0178	-
1.3131	23500	0.0185	-
1.3159	23550	0.021	-
1.3187	23600	0.019	-
1.3215	23650	0.0219	-
1.3242	23700	0.0188	-
1.3270	23750	0.0213	-
1.3298	23800	0.0214	-
1.3326	23850	0.0205	-
1.3354	23900	0.0215	-
1.3382	23950	0.0215	-
1.3410	24000	0.0221	-
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1.3466	24100	0.0185	-
1.3494	24150	0.0207	-
1.3522	24200	0.0209	-
1.3550	24250	0.02	-
1.3578	24300	0.0182	-
1.3606	24350	0.0226	-
1.3634	24400	0.0186	-
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1.3689	24500	0.021	-
1.3717	24550	0.0203	-
1.3745	24600	0.0181	-
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1.3801	24700	0.0174	-
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1.3885	24850	0.0198	-
1.3913	24900	0.0184	-
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1.3969	25000	0.0192	-
1.3997	25050	0.0222	-
1.4025	25100	0.0213	-
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1.4081	25200	0.0177	-
1.4109	25250	0.0206	-
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1.4192	25400	0.0188	-
1.4220	25450	0.0167	-
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1.4416	25800	0.0184	-
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1.4528	26000	0.0176	-
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1.4611	26150	0.0179	-
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1.4695	26300	0.0184	-
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1.4751	26400	0.0179	-
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1.4863	26600	0.0191	-
1.4891	26650	0.0203	-
1.4919	26700	0.0209	-
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1.4975	26800	0.0192	-
1.5003	26850	0.0194	-
1.5030	26900	0.0197	-
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1.5086	27000	0.0167	-
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1.5198	27200	0.0188	-
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1.5310	27400	0.018	-
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1.5422	27600	0.0189	-
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1.5533	27800	0.0181	-
1.5561	27850	0.0186	-
1.5589	27900	0.019	-
1.5617	27950	0.0202	-
1.5645	28000	0.0159	-
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1.5701	28100	0.0182	-
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1.5813	28300	0.0179	-
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1.5924	28500	0.0213	-
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1.6092	28800	0.0196	-
1.6120	28850	0.0176	-
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1.6204	29000	0.0161	-
1.6232	29050	0.0192	-
1.6260	29100	0.0159	-
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1.6316	29200	0.0193	-
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1.6427	29400	0.0184	-
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1.6511	29550	0.0175	-
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1.6595	29700	0.0199	-
1.6623	29750	0.0182	-
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1.6735	29950	0.0189	-
1.6763	30000	0.0185	-
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1.6818	30100	0.0161	-
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1.6902	30250	0.0158	-
1.6930	30300	0.0199	-
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1.6986	30400	0.02	-
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1.7042	30500	0.0194	-
1.7070	30550	0.018	-
1.7098	30600	0.0163	-
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1.7210	30800	0.018	-
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1.7321	31000	0.0181	-
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1.7405	31150	0.0155	-
1.7433	31200	0.0178	-
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1.7517	31350	0.0198	-
1.7545	31400	0.0195	-
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1.7601	31500	0.0186	-
1.7629	31550	0.0161	-
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1.7685	31650	0.0179	-
1.7712	31700	0.0173	-
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1.7796	31850	0.0167	-
1.7824	31900	0.015	-
1.7852	31950	0.0175	-
1.7880	32000	0.0184	-
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1.7936	32100	0.0183	-
1.7964	32150	0.0169	-
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1.8020	32250	0.0185	-
1.8048	32300	0.0164	-
1.8076	32350	0.0162	-
1.8104	32400	0.0165	-
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1.8159	32500	0.017	-
1.8187	32550	0.0181	-
1.8215	32600	0.0168	-
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1.8271	32700	0.0192	-
1.8299	32750	0.0167	-
1.8327	32800	0.0179	-
1.8355	32850	0.0184	-
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1.8411	32950	0.0177	-
1.8439	33000	0.017	-
1.8467	33050	0.0192	-
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1.8523	33150	0.0198	-
1.8551	33200	0.0166	-
1.8579	33250	0.017	-
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1.8634	33350	0.0184	-
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1.8718	33500	0.0176	-
1.8746	33550	0.0171	-
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1.8830	33700	0.0174	-
1.8858	33750	0.0162	-
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1.8942	33900	0.0189	-
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1.9026	34050	0.0168	-
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1.9081	34150	0.0162	-
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1.9333	34600	0.0173	-
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1.9417	34750	0.0173	-
1.9445	34800	0.0157	-
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1.9500	34900	0.0169	-
1.9528	34950	0.0189	-
1.9556	35000	0.017	-
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1.9612	35100	0.016	-
1.9640	35150	0.0178	-
1.9668	35200	0.0167	-
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1.9724	35300	0.0196	-
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1.9780	35400	0.0174	-
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1.9836	35500	0.0163	-
1.9864	35550	0.0153	-
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1.9920	35650	0.0178	-
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1.9975	35750	0.0199	-
2.0003	35800	0.0158	-
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2.0394	36500	0.018	-
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2.0618	36900	0.0195	-
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2.2322	39950	0.016	-
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2.2406	40100	0.0184	-
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2.2909	41000	0.0164	-
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2.2993	41150	0.0181	-
2.3021	41200	0.017	-
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2.3104	41350	0.0165	-
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2.3188	41500	0.0166	-
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2.3803	42600	0.0166	-
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2.3887	42750	0.0172	-
2.3915	42800	0.016	-
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2.3970	42900	0.0163	-
2.3998	42950	0.018	-
2.4026	43000	0.0171	-
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2.4110	43150	0.0194	-
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2.4585	44000	0.0156	-
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2.4892	44550	0.0167	-
2.4920	44600	0.0173	-
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2.5004	44750	0.016	-
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2.5395	45450	0.0162	-
2.5423	45500	0.0172	-
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2.5479	45600	0.017	-
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2.5535	45700	0.0166	-
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2.5591	45800	0.0171	-
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2.5703	46000	0.0157	-
2.5731	46050	0.0166	-
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2.5814	46200	0.018	-
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2.5898	46350	0.0171	-
2.5926	46400	0.0172	-
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2.6038	46600	0.0187	-
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2.6122	46750	0.0171	-
2.6150	46800	0.0171	-
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2.6261	47000	0.0171	-
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2.6317	47100	0.0159	-
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2.6401	47250	0.0156	-
2.6429	47300	0.0171	-
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2.6485	47400	0.019	-
2.6513	47450	0.0158	-
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2.6597	47600	0.018	-
2.6625	47650	0.018	-
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2.6680	47750	0.0179	-
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2.6820	48000	0.016	-
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2.6960	48250	0.0166	-
2.6988	48300	0.0153	-
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2.7072	48450	0.0158	-
2.7100	48500	0.0175	-
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2.7211	48700	0.0159	-
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2.7323	48900	0.0172	-
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2.7435	49100	0.016	-
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2.7519	49250	0.0168	-
2.7547	49300	0.0158	-
2.7574	49350	0.0176	-
2.7602	49400	0.0158	-
2.7630	49450	0.017	-
2.7658	49500	0.0166	-
2.7686	49550	0.0176	-
2.7714	49600	0.0176	-
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4.1906	75000	0.016	-
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9.7419	174350	0.0153	-
9.7446	174400	0.0178	-
9.7474	174450	0.0149	-
9.7502	174500	0.0145	-
9.7530	174550	0.0162	-
9.7558	174600	0.0165	-
9.7586	174650	0.0162	-
9.7614	174700	0.0174	-
9.7642	174750	0.0156	-
9.7670	174800	0.0142	-
9.7698	174850	0.0151	-
9.7726	174900	0.0151	-
9.7754	174950	0.0165	-
9.7782	175000	0.0157	-
9.7810	175050	0.0137	-
9.7838	175100	0.0177	-
9.7866	175150	0.014	-
9.7894	175200	0.0146	-
9.7921	175250	0.0148	-
9.7949	175300	0.0143	-
9.7977	175350	0.015	-
9.8005	175400	0.0158	-
9.8033	175450	0.0161	-
9.8061	175500	0.0149	-
9.8089	175550	0.0162	-
9.8117	175600	0.0152	-
9.8145	175650	0.016	-
9.8173	175700	0.0144	-
9.8201	175750	0.0153	-
9.8229	175800	0.0164	-
9.8257	175850	0.0154	-
9.8285	175900	0.0145	-
9.8313	175950	0.0179	-
9.8341	176000	0.0164	-
9.8368	176050	0.0167	-
9.8396	176100	0.0163	-
9.8424	176150	0.015	-
9.8452	176200	0.0142	-
9.8480	176250	0.0149	-
9.8508	176300	0.0154	-
9.8536	176350	0.0157	-
9.8564	176400	0.0152	-
9.8592	176450	0.0162	-
9.8620	176500	0.0157	-
9.8648	176550	0.0146	-
9.8676	176600	0.0168	-
9.8704	176650	0.0153	-
9.8732	176700	0.0152	-
9.8760	176750	0.0154	-
9.8788	176800	0.0142	-
9.8815	176850	0.0147	-
9.8843	176900	0.015	-
9.8871	176950	0.0158	-
9.8899	177000	0.0152	-
9.8927	177050	0.0162	-
9.8955	177100	0.0145	-
9.8983	177150	0.0144	-
9.9011	177200	0.015	-
9.9039	177250	0.0176	-
9.9067	177300	0.0145	-
9.9095	177350	0.0167	-
9.9123	177400	0.0154	-
9.9151	177450	0.0148	-
9.9179	177500	0.0157	-
9.9207	177550	0.0137	-
9.9235	177600	0.0162	-
9.9262	177650	0.0163	-
9.9290	177700	0.0168	-
9.9318	177750	0.0142	-
9.9346	177800	0.016	-
9.9374	177850	0.0158	-
9.9402	177900	0.0154	-
9.9430	177950	0.0163	-
9.9458	178000	0.016	-
9.9486	178050	0.0142	-
9.9514	178100	0.0164	-
9.9542	178150	0.0165	-
9.9570	178200	0.015	-
9.9598	178250	0.0148	-
9.9626	178300	0.0158	-
9.9654	178350	0.0151	-
9.9682	178400	0.0163	-
9.9709	178450	0.0156	-
9.9737	178500	0.0161	-
9.9765	178550	0.0163	-
9.9793	178600	0.0143	-
9.9821	178650	0.0158	-
9.9849	178700	0.0161	-
9.9877	178750	0.0156	-
9.9905	178800	0.0156	-
9.9933	178850	0.0165	-
9.9961	178900	0.015	-
9.9989	178950	0.0185	-

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}
}