Amadeus99/indonesia-election-sentiment-classification-setfit-mt5-small-3

SetFit with indonlp/cendol-mt5-small-inst

This is a SetFit model that can be used for Text Classification. This SetFit model uses indonlp/cendol-mt5-small-inst as the Sentence Transformer embedding model. A LogisticRegression 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: indonlp/cendol-mt5-small-inst
• Classification head: a LogisticRegression instance
• Maximum Sequence Length: None tokens
• Number of Classes: 3 classes

Model Sources

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

Model Labels

Label	Examples
positif	'mas bro, yang lagi sibuk mikirin pilpres 2024, ini solusinya! pilih ganjar pranowo - mahfud md. dia punya program kece: disabilitas bisa mandiri dan berprestasi, plus ada program 1 desa 1 mobil akses. bikin perubahan!#jnk #ganjarmahfudrebound #ganjarpranowopilihanumat' 'semoga tambah umur makin baik dan sukses selalu mas ganjar mahfud hebat #l3bihbaik #mahfudlebihbaik3 #ganjarmahfud2024 nelayan sejahtera' 'capres terhumble dan selalu mau terjun ke lapangan hanya untuk rakyatnya, no debat kalau pak ganjar emang mendedikasikan hidupnya untuk rakyat'
netral	'minta pak ganjar' 'mas alam penerus pak ganjar pranowo 🥰' 'seruu liat pak ganjar bakar ikan bareng nelayan'
negatif	'dan anda selalu selalu sibuk dgn elektabilitas ganjar mahfud krn partai anda dipastikan hanya jd pemandu sorak di pemilu 2024. paham kau, dungu !!😝😝😂😂' 'ganjar varian wasit epl iki cen ra mutu tenan' '"kami sangat keberatan bila penyuka bokep spt ganjar pranowo disamakan dg sosok khalifah umar bin khattab mahfud hebat #l3bihbaik #mahfudlebihbaik3 #ganjarmahfud2024 buruh naik kelas'

Evaluation

Metrics

Label	Accuracy	F1
all	0.7412	0.7324

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("Amadeus99/indonesia-election-sentiment-classification-setfit-mt5-small-3")
# Run inference
preds = model("prabowo ada gk sih di.acara kadin ini? pak ganjar ada, muncul jg di yutub... pak prabowo ada gak ?")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	3	29.1233	727

Label	Training Sample Count
negatif	51
netral	55
positif	194

Training Hyperparameters

• batch_size: (2, 2)
• num_epochs: (7, 7)
• max_steps: -1
• sampling_strategy: oversampling
• 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
• seed: 42
• eval_max_steps: -1
• load_best_model_at_end: True

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0000	1	0.1884	-
0.0021	50	0.1494	-
0.0043	100	0.0904	-
0.0064	150	0.3429	-
0.0086	200	0.2184	-
0.0107	250	0.2459	-
0.0128	300	0.1696	-
0.0150	350	0.3324	-
0.0171	400	0.2217	-
0.0193	450	0.2635	-
0.0214	500	0.2711	-
0.0235	550	0.252	-
0.0257	600	0.3589	-
0.0278	650	0.129	-
0.0300	700	0.371	-
0.0321	750	0.3452	-
0.0342	800	0.229	-
0.0364	850	0.2998	-
0.0385	900	0.1768	-
0.0407	950	0.245	-
0.0428	1000	0.3219	-
0.0449	1050	0.2776	-
0.0471	1100	0.1954	-
0.0492	1150	0.2672	-
0.0514	1200	0.2363	-
0.0535	1250	0.2324	-
0.0556	1300	0.1223	-
0.0578	1350	0.3191	-
0.0599	1400	0.2674	-
0.0620	1450	0.3218	-
0.0642	1500	0.3459	-
0.0663	1550	0.262	-
0.0685	1600	0.3139	-
0.0706	1650	0.3333	-
0.0727	1700	0.3297	-
0.0749	1750	0.2094	-
0.0770	1800	0.2315	-
0.0792	1850	0.3361	-
0.0813	1900	0.3309	-
0.0834	1950	0.1621	-
0.0856	2000	0.2885	-
0.0877	2050	0.1736	-
0.0899	2100	0.1998	-
0.0920	2150	0.2244	-
0.0941	2200	0.2741	-
0.0963	2250	0.2661	-
0.0984	2300	0.3921	-
0.1006	2350	0.1175	-
0.1027	2400	0.2447	-
0.1048	2450	0.2181	-
0.1070	2500	0.3438	-
0.1091	2550	0.1245	-
0.1113	2600	0.3104	-
0.1134	2650	0.23	-
0.1155	2700	0.3043	-
0.1177	2750	0.1099	-
0.1198	2800	0.1814	-
0.1220	2850	0.3757	-
0.1241	2900	0.3294	-
0.1262	2950	0.1029	-
0.1284	3000	0.2072	-
0.1305	3050	0.405	-
0.1327	3100	0.3449	-
0.1348	3150	0.2474	-
0.1369	3200	0.1398	-
0.1391	3250	0.1798	-
0.1412	3300	0.4131	-
0.1434	3350	0.3042	-
0.1455	3400	0.3202	-
0.1476	3450	0.0942	-
0.1498	3500	0.3273	-
0.1519	3550	0.1324	-
0.1541	3600	0.124	-
0.1562	3650	0.2416	-
0.1583	3700	0.2863	-
0.1605	3750	0.5035	-
0.1626	3800	0.2491	-
0.1647	3850	0.3656	-
0.1669	3900	0.1244	-
0.1690	3950	0.304	-
0.1712	4000	0.1909	-
0.1733	4050	0.0789	-
0.1754	4100	0.2741	-
0.1776	4150	0.2804	-
0.1797	4200	0.1466	-
0.1819	4250	0.2342	-
0.1840	4300	0.1654	-
0.1861	4350	0.1873	-
0.1883	4400	0.261	-
0.1904	4450	0.3579	-
0.1926	4500	0.4696	-
0.1947	4550	0.1058	-
0.1968	4600	0.2341	-
0.1990	4650	0.3211	-
0.2011	4700	0.2251	-
0.2033	4750	0.1729	-
0.2054	4800	0.1636	-
0.2075	4850	0.2094	-
0.2097	4900	0.1908	-
0.2118	4950	0.1742	-
0.2140	5000	0.1286	-
0.2161	5050	0.1781	-
0.2182	5100	0.1194	-
0.2204	5150	0.2974	-
0.2225	5200	0.2146	-
0.2247	5250	0.1063	-
0.2268	5300	0.1675	-
0.2289	5350	0.2253	-
0.2311	5400	0.2306	-
0.2332	5450	0.0524	-
0.2354	5500	0.0981	-
0.2375	5550	0.1069	-
0.2396	5600	0.1743	-
0.2418	5650	0.0512	-
0.2439	5700	0.2756	-
0.2461	5750	0.0521	-
0.2482	5800	0.08	-
0.2503	5850	0.2502	-
0.2525	5900	0.1537	-
0.2546	5950	0.1381	-
0.2568	6000	0.1516	-
0.2589	6050	0.0827	-
0.2610	6100	0.3652	-
0.2632	6150	0.1049	-
0.2653	6200	0.1592	-
0.2674	6250	0.1984	-
0.2696	6300	0.205	-
0.2717	6350	0.0565	-
0.2739	6400	0.136	-
0.2760	6450	0.0637	-
0.2781	6500	0.0385	-
0.2803	6550	0.1204	-
0.2824	6600	0.1001	-
0.2846	6650	0.0681	-
0.2867	6700	0.0271	-
0.2888	6750	0.2097	-
0.2910	6800	0.1655	-
0.2931	6850	0.2004	-
0.2953	6900	0.0148	-
0.2974	6950	0.0751	-
0.2995	7000	0.1135	-
0.3017	7050	0.2834	-
0.3038	7100	0.2541	-
0.3060	7150	0.0359	-
0.3081	7200	0.0967	-
0.3102	7250	0.0833	-
0.3124	7300	0.0492	-
0.3145	7350	0.0646	-
0.3167	7400	0.0516	-
0.3188	7450	0.1974	-
0.3209	7500	0.0176	-
0.3231	7550	0.1451	-
0.3252	7600	0.0494	-
0.3274	7650	0.2884	-
0.3295	7700	0.0796	-
0.3316	7750	0.038	-
0.3338	7800	0.024	-
0.3359	7850	0.2396	-
0.3381	7900	0.0136	-
0.3402	7950	0.1104	-
0.3423	8000	0.0426	-
0.3445	8050	0.0173	-
0.3466	8100	0.07	-
0.3488	8150	0.1223	-
0.3509	8200	0.1015	-
0.3530	8250	0.0069	-
0.3552	8300	0.0805	-
0.3573	8350	0.0857	-
0.3595	8400	0.0118	-
0.3616	8450	0.0184	-
0.3637	8500	0.0062	-
0.3659	8550	0.4361	-
0.3680	8600	0.1756	-
0.3701	8650	0.0206	-
0.3723	8700	0.4301	-
0.3744	8750	0.0092	-
0.3766	8800	0.0015	-
0.3787	8850	0.0703	-
0.3808	8900	0.0346	-
0.3830	8950	0.2098	-
0.3851	9000	0.0305	-
0.3873	9050	0.194	-
0.3894	9100	0.0129	-
0.3915	9150	0.0442	-
0.3937	9200	0.0161	-
0.3958	9250	0.0309	-
0.3980	9300	0.0418	-
0.4001	9350	0.0737	-
0.4022	9400	0.0826	-
0.4044	9450	0.0488	-
0.4065	9500	0.238	-
0.4087	9550	0.0508	-
0.4108	9600	0.0535	-
0.4129	9650	0.2539	-
0.4151	9700	0.0028	-
0.4172	9750	0.1599	-
0.4194	9800	0.0195	-
0.4215	9850	0.018	-
0.4236	9900	0.0009	-
0.4258	9950	0.0531	-
0.4279	10000	0.0075	-
0.4301	10050	0.013	-
0.4322	10100	0.0151	-
0.4343	10150	0.0421	-
0.4365	10200	0.1041	-
0.4386	10250	0.0449	-
0.4408	10300	0.005	-
0.4429	10350	0.0088	-
0.4450	10400	0.0036	-
0.4472	10450	0.071	-
0.4493	10500	0.0098	-
0.4515	10550	0.005	-
0.4536	10600	0.0442	-
0.4557	10650	0.0614	-
0.4579	10700	0.0048	-
0.4600	10750	0.001	-
0.4622	10800	0.0094	-
0.4643	10850	0.0039	-
0.4664	10900	0.0123	-
0.4686	10950	0.0013	-
0.4707	11000	0.0031	-
0.4728	11050	0.0008	-
0.4750	11100	0.004	-
0.4771	11150	0.0008	-
0.4793	11200	0.1188	-
0.4814	11250	0.0097	-
0.4835	11300	0.002	-
0.4857	11350	0.0065	-
0.4878	11400	0.0581	-
0.4900	11450	0.0014	-
0.4921	11500	0.082	-
0.4942	11550	0.0042	-
0.4964	11600	0.0027	-
0.4985	11650	0.0469	-
0.5007	11700	0.0088	-
0.5028	11750	0.0045	-
0.5049	11800	0.001	-
0.5071	11850	0.0019	-
0.5092	11900	0.5739	-
0.5114	11950	0.0023	-
0.5135	12000	0.0026	-
0.5156	12050	0.0047	-
0.5178	12100	0.0006	-
0.5199	12150	0.4686	-
0.5221	12200	0.0308	-
0.5242	12250	0.0044	-
0.5263	12300	0.0004	-
0.5285	12350	0.0048	-
0.5306	12400	0.0115	-
0.5328	12450	0.0081	-
0.5349	12500	0.6468	-
0.5370	12550	0.0068	-
0.5392	12600	0.0002	-
0.5413	12650	0.1194	-
0.5435	12700	0.0025	-
0.5456	12750	0.0004	-
0.5477	12800	0.284	-
0.5499	12850	0.0027	-
0.5520	12900	0.005	-
0.5542	12950	0.0011	-
0.5563	13000	0.2522	-
0.5584	13050	0.0061	-
0.5606	13100	0.0038	-
0.5627	13150	0.0009	-
0.5649	13200	0.0013	-
0.5670	13250	0.2542	-
0.5691	13300	0.1878	-
0.5713	13350	0.0883	-
0.5734	13400	0.0029	-
0.5755	13450	0.001	-
0.5777	13500	0.0013	-
0.5798	13550	0.0009	-
0.5820	13600	0.0041	-
0.5841	13650	0.1063	-
0.5862	13700	0.0009	-
0.5884	13750	0.0729	-
0.5905	13800	0.0021	-
0.5927	13850	0.0035	-
0.5948	13900	0.0059	-
0.5969	13950	0.0006	-
0.5991	14000	0.0025	-
0.6012	14050	0.0975	-
0.6034	14100	0.0249	-
0.6055	14150	0.0044	-
0.6076	14200	0.0004	-
0.6098	14250	0.0898	-
0.6119	14300	0.0867	-
0.6141	14350	0.0013	-
0.6162	14400	0.0004	-
0.6183	14450	0.0031	-
0.6205	14500	0.0032	-
0.6226	14550	0.0388	-
0.6248	14600	0.0027	-
0.6269	14650	0.4814	-
0.6290	14700	0.0042	-
0.6312	14750	0.0036	-
0.6333	14800	0.223	-
0.6355	14850	0.0	-
0.6376	14900	0.0176	-
0.6397	14950	0.1307	-
0.6419	15000	0.0731	-
0.6440	15050	0.001	-
0.6462	15100	0.3446	-
0.6483	15150	0.312	-
0.6504	15200	0.0005	-
0.6526	15250	0.0002	-
0.6547	15300	0.004	-
0.6569	15350	0.0023	-
0.6590	15400	0.002	-
0.6611	15450	0.0012	-
0.6633	15500	0.0009	-
0.6654	15550	0.0005	-
0.6676	15600	0.0032	-
0.6697	15650	0.0014	-
0.6718	15700	0.0013	-
0.6740	15750	0.0163	-
0.6761	15800	0.0007	-
0.6782	15850	0.3503	-
0.6804	15900	0.0011	-
0.6825	15950	0.0018	-
0.6847	16000	0.0008	-
0.6868	16050	0.0014	-
0.6889	16100	0.0005	-
0.6911	16150	0.0022	-
0.6932	16200	0.002	-
0.6954	16250	0.0015	-
0.6975	16300	0.0004	-
0.6996	16350	0.0057	-
0.7018	16400	0.0002	-
0.7039	16450	0.0005	-
0.7061	16500	0.0799	-
0.7082	16550	0.0672	-
0.7103	16600	0.0005	-
0.7125	16650	0.484	-
0.7146	16700	0.0007	-
0.7168	16750	0.0003	-
0.7189	16800	0.0002	-
0.7210	16850	0.022	-
0.7232	16900	0.0002	-
0.7253	16950	0.0011	-
0.7275	17000	0.0009	-
0.7296	17050	0.0045	-
0.7317	17100	0.0007	-
0.7339	17150	0.001	-
0.7360	17200	0.0009	-
0.7382	17250	0.0019	-
0.7403	17300	0.0005	-
0.7424	17350	0.0654	-
0.7446	17400	0.0003	-
0.7467	17450	0.0025	-
0.7489	17500	0.0026	-
0.7510	17550	0.0113	-
0.7531	17600	0.0012	-
0.7553	17650	0.0018	-
0.7574	17700	0.0017	-
0.7596	17750	0.0049	-
0.7617	17800	0.0002	-
0.7638	17850	0.0005	-
0.7660	17900	0.0006	-
0.7681	17950	0.0001	-
0.7703	18000	0.0004	-
0.7724	18050	0.0011	-
0.7745	18100	0.0019	-
0.7767	18150	0.0013	-
0.7788	18200	0.0013	-
0.7809	18250	0.0024	-
0.7831	18300	0.0091	-
0.7852	18350	0.0005	-
0.7874	18400	0.0017	-
0.7895	18450	0.0001	-
0.7916	18500	0.0005	-
0.7938	18550	0.0009	-
0.7959	18600	0.0004	-
0.7981	18650	0.0006	-
0.8002	18700	0.0002	-
0.8023	18750	0.0003	-
0.8045	18800	0.0004	-
0.8066	18850	0.0006	-
0.8088	18900	0.0002	-
0.8109	18950	0.0004	-
0.8130	19000	0.0004	-
0.8152	19050	0.0011	-
0.8173	19100	0.0001	-
0.8195	19150	0.0037	-
0.8216	19200	0.0006	-
0.8237	19250	0.0008	-
0.8259	19300	0.0002	-
0.8280	19350	0.0004	-
0.8302	19400	0.0003	-
0.8323	19450	0.0003	-
0.8344	19500	0.0036	-
0.8366	19550	0.0032	-
0.8387	19600	0.0006	-
0.8409	19650	0.0059	-
0.8430	19700	0.0003	-
0.8451	19750	0.0019	-
0.8473	19800	0.0301	-
0.8494	19850	0.0002	-
0.8516	19900	0.0033	-
0.8537	19950	0.0005	-
0.8558	20000	0.0004	-
0.8580	20050	0.0002	-
0.8601	20100	0.0069	-
0.8623	20150	0.0002	-
0.8644	20200	0.0013	-
0.8665	20250	0.0002	-
0.8687	20300	0.0002	-
0.8708	20350	0.0003	-
0.8730	20400	0.0001	-
0.8751	20450	0.0001	-
0.8772	20500	0.0	-
0.8794	20550	0.0001	-
0.8815	20600	0.0003	-
0.8836	20650	0.0002	-
0.8858	20700	0.0003	-
0.8879	20750	0.0043	-
0.8901	20800	0.0001	-
0.8922	20850	0.0009	-
0.8943	20900	0.0021	-
0.8965	20950	0.0004	-
0.8986	21000	0.0005	-
0.9008	21050	0.0013	-
0.9029	21100	0.0012	-
0.9050	21150	0.0003	-
0.9072	21200	0.0025	-
0.9093	21250	0.0007	-
0.9115	21300	0.0017	-
0.9136	21350	0.0005	-
0.9157	21400	0.0002	-
0.9179	21450	0.0019	-
0.9200	21500	0.0001	-
0.9222	21550	0.0001	-
0.9243	21600	0.0023	-
0.9264	21650	0.0021	-
0.9286	21700	0.0007	-
0.9307	21750	0.0001	-
0.9329	21800	0.0001	-
0.9350	21850	0.0001	-
0.9371	21900	0.0001	-
0.9393	21950	0.0001	-
0.9414	22000	0.0003	-
0.9436	22050	0.0	-
0.9457	22100	0.0001	-
0.9478	22150	0.0034	-
0.9500	22200	0.0006	-
0.9521	22250	0.0001	-
0.9543	22300	0.0003	-
0.9564	22350	0.0001	-
0.9585	22400	0.0006	-
0.9607	22450	0.0002	-
0.9628	22500	0.0003	-
0.9650	22550	0.0001	-
0.9671	22600	0.0001	-
0.9692	22650	0.0001	-
0.9714	22700	0.0001	-
0.9735	22750	0.0004	-
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6.6520	155450	0.0	-
6.6541	155500	0.0	-
6.6563	155550	0.0	-
6.6584	155600	0.0002	-
6.6605	155650	0.0	-
6.6627	155700	0.0	-
6.6648	155750	0.0	-
6.6670	155800	0.0	-
6.6691	155850	0.0001	-
6.6712	155900	0.0	-
6.6734	155950	0.0	-
6.6755	156000	0.0	-
6.6776	156050	0.0	-
6.6798	156100	0.0	-
6.6819	156150	0.0	-
6.6841	156200	0.0	-
6.6862	156250	0.0	-
6.6883	156300	0.0001	-
6.6905	156350	0.0	-
6.6926	156400	0.0	-
6.6948	156450	0.0	-
6.6969	156500	0.0	-
6.6990	156550	0.0	-
6.7012	156600	0.0	-
6.7033	156650	0.0	-
6.7055	156700	0.0001	-
6.7076	156750	0.0	-
6.7097	156800	0.0	-
6.7119	156850	0.0	-
6.7140	156900	0.0001	-
6.7162	156950	0.0	-
6.7183	157000	0.0	-
6.7204	157050	0.0	-
6.7226	157100	0.0	-
6.7247	157150	0.0	-
6.7269	157200	0.0001	-
6.7290	157250	0.0	-
6.7311	157300	0.0	-
6.7333	157350	0.0	-
6.7354	157400	0.0	-
6.7376	157450	0.0	-
6.7397	157500	0.0	-
6.7418	157550	0.0001	-
6.7440	157600	0.0	-
6.7461	157650	0.0	-
6.7483	157700	0.0	-
6.7504	157750	0.0001	-
6.7525	157800	0.0	-
6.7547	157850	0.0	-
6.7568	157900	0.0	-
6.7590	157950	0.0	-
6.7611	158000	0.0	-
6.7632	158050	0.0	-
6.7654	158100	0.0	-
6.7675	158150	0.0	-
6.7697	158200	0.0	-
6.7718	158250	0.0	-
6.7739	158300	0.0	-
6.7761	158350	0.0	-
6.7782	158400	0.0	-
6.7804	158450	0.0	-
6.7825	158500	0.0001	-
6.7846	158550	0.0	-
6.7868	158600	0.0	-
6.7889	158650	0.0	-
6.7910	158700	0.0	-
6.7932	158750	0.0	-
6.7953	158800	0.0	-
6.7975	158850	0.0	-
6.7996	158900	0.0	-
6.8017	158950	0.0	-
6.8039	159000	0.0	-
6.8060	159050	0.0	-
6.8082	159100	0.0004	-
6.8103	159150	0.0	-
6.8124	159200	0.0	-
6.8146	159250	0.0	-
6.8167	159300	0.0	-
6.8189	159350	0.0	-
6.8210	159400	0.0	-
6.8231	159450	0.0	-
6.8253	159500	0.0	-
6.8274	159550	0.0	-
6.8296	159600	0.0	-
6.8317	159650	0.0	-
6.8338	159700	0.0	-
6.8360	159750	0.0	-
6.8381	159800	0.0	-
6.8403	159850	0.0	-
6.8424	159900	0.0	-
6.8445	159950	0.0	-
6.8467	160000	0.0	-
6.8488	160050	0.0001	-
6.8510	160100	0.0001	-
6.8531	160150	0.0	-
6.8552	160200	0.0	-
6.8574	160250	0.0	-
6.8595	160300	0.0	-
6.8617	160350	0.0	-
6.8638	160400	0.0	-
6.8659	160450	0.0	-
6.8681	160500	0.0	-
6.8702	160550	0.0	-
6.8724	160600	0.0	-
6.8745	160650	0.0	-
6.8766	160700	0.0	-
6.8788	160750	0.0	-
6.8809	160800	0.0	-
6.8831	160850	0.0	-
6.8852	160900	0.0	-
6.8873	160950	0.0	-
6.8895	161000	0.0	-
6.8916	161050	0.0	-
6.8937	161100	0.0	-
6.8959	161150	0.0001	-
6.8980	161200	0.0	-
6.9002	161250	0.0	-
6.9023	161300	0.0	-
6.9044	161350	0.0	-
6.9066	161400	0.0	-
6.9087	161450	0.0	-
6.9109	161500	0.0	-
6.9130	161550	0.0	-
6.9151	161600	0.0	-
6.9173	161650	0.0	-
6.9194	161700	0.0	-
6.9216	161750	0.0	-
6.9237	161800	0.0	-
6.9258	161850	0.0	-
6.9280	161900	0.0001	-
6.9301	161950	0.0	-
6.9323	162000	0.0	-
6.9344	162050	0.0	-
6.9365	162100	0.0	-
6.9387	162150	0.0001	-
6.9408	162200	0.0	-
6.9430	162250	0.0	-
6.9451	162300	0.0	-
6.9472	162350	0.0	-
6.9494	162400	0.0	-
6.9515	162450	0.0	-
6.9537	162500	0.0	-
6.9558	162550	0.0	-
6.9579	162600	0.0	-
6.9601	162650	0.0	-
6.9622	162700	0.0	-
6.9644	162750	0.0	-
6.9665	162800	0.0	-
6.9686	162850	0.0	-
6.9708	162900	0.0001	-
6.9729	162950	0.0	-
6.9751	163000	0.0	-
6.9772	163050	0.0	-
6.9793	163100	0.0	-
6.9815	163150	0.0	-
6.9836	163200	0.0	-
6.9858	163250	0.0	-
6.9879	163300	0.0	-
6.9900	163350	0.0	-
6.9922	163400	0.0	-
6.9943	163450	0.0	-
6.9964	163500	0.0	-
6.9986	163550	0.0	-
7.0	163583	-	0.288

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.10.13
• SetFit: 1.0.3
• Sentence Transformers: 3.0.1
• Transformers: 4.41.2
• PyTorch: 2.1.2
• Datasets: 2.19.2
• Tokenizers: 0.19.1

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}
}