Amadeus99/indonesia-election-sentiment-classification-setfit-1

SetFit with indolem/indobertweet-base-uncased

This is a SetFit model that can be used for Text Classification. This SetFit model uses indolem/indobertweet-base-uncased 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: indolem/indobertweet-base-uncased
• Classification head: a LogisticRegression instance
• Maximum Sequence Length: 512 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
netral	"gak ada chan's room...kali ini diganti pak anies live on tiktok 😭 bacain petuah2nya berasa si ngabchan ksh petuah 🥲" 'media asing sorot anies baswedan di pilpres ri, ada apa?' 'yg dari tiktok pak anies ini sepertinya lintas fandom, k-pop general, krn nyambung aja. bkn berasal dari satu fandom tertentu, jadi act of personal. tdk bawa fandom asal, krn fandom asal biasanya g suka nama fandom dibawa ke politik, kecuali utk misi kemanusiaan / gerakan sosial.'
positif	'kalo maksudnya ke smrc, ini serangan menarik. anies pernah diajak saiful kerja di lsi. saiful juga ikut bantu anies jadi rektor paramadina. ikut bantu danai program beasiswa yg dia bikin. gak pernah ada terima kasih. dapet data2 survei gratis. dia jadi capres juga karena data2 survei. giliran survei buruk, nyerang kek begini. [re muchlis_ar]' 'manies cukup rnb aja akun tsb ya gausah interact nanti akunnya ga ilang ilang, jangan jadi war juga takutnya boomerang ke kita! ayo kita hype uri anies yang baik baik aja uri anies selalu ngajarin buat berbuat hal baik aja 💐💗 [re jpremacyy]' 'dalam kasus anies, ketakutan itu dipelihara sekalipun selama 5 tahun di dki, anies sudah buktikan ketakutan itu tdk terbukti. yg ada dicari-cari kesalahan dgn bilang anies menikmati polarisasi dsb. ini ketakutan apa dendam yg dipelihara? [re yanua02]'
negatif	'[ desak anies ] segmen i gimana, nies? apa yang mau dibuat untuk sumbar? "orang berakal hidup untuk masyarakatnya, bukan buat dirinya sendiri," by buya hamka [re bospurwa]' 'dendam kesumat ke anies ,semoga membawa partainya ikut sekarat.' 'gw sih setuju, tapi kok ya orang2 berharap anies ini harus jadi malaikat? yang 2 lainnya itu bukan capres kah? [re madhink]'

Evaluation

Metrics

Label	Accuracy	F1
all	0.5806	0.5834

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-1")
# Run inference
preds = model("muzani menilai, anies telah melupakan jasa prabowo yang membuatnya terpilih menjadi gubernur dki jakarta pada 2017. [re kompascom]")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	1	25.33	282

Label	Training Sample Count
negatif	115
netral	101
positif	84

Training Hyperparameters

• batch_size: (8, 8)
• 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
0.0001	1	0.265	-
0.0067	50	0.2738	-
0.0134	100	0.282	-
0.0202	150	0.2796	-
0.0269	200	0.2402	-
0.0336	250	0.2733	-
0.0403	300	0.2871	-
0.0470	350	0.2678	-
0.0538	400	0.2872	-
0.0605	450	0.2394	-
0.0672	500	0.2324	-
0.0739	550	0.2344	-
0.0806	600	0.2963	-
0.0874	650	0.2555	-
0.0941	700	0.2599	-
0.1008	750	0.2775	-
0.1075	800	0.1988	-
0.1142	850	0.2473	-
0.1210	900	0.2309	-
0.1277	950	0.2269	-
0.1344	1000	0.2531	-
0.1411	1050	0.2247	-
0.1478	1100	0.1909	-
0.1546	1150	0.2172	-
0.1613	1200	0.225	-
0.1680	1250	0.1672	-
0.1747	1300	0.254	-
0.1815	1350	0.2084	-
0.1882	1400	0.1838	-
0.1949	1450	0.2212	-
0.2016	1500	0.2326	-
0.2083	1550	0.2559	-
0.2151	1600	0.1399	-
0.2218	1650	0.1764	-
0.2285	1700	0.2307	-
0.2352	1750	0.2066	-
0.2419	1800	0.0804	-
0.2487	1850	0.0888	-
0.2554	1900	0.0795	-
0.2621	1950	0.148	-
0.2688	2000	0.1514	-
0.2755	2050	0.0869	-
0.2823	2100	0.0478	-
0.2890	2150	0.0165	-
0.2957	2200	0.0117	-
0.3024	2250	0.0021	-
0.3091	2300	0.0019	-
0.3159	2350	0.0015	-
0.3226	2400	0.0012	-
0.3293	2450	0.0009	-
0.3360	2500	0.0007	-
0.3427	2550	0.0002	-
0.3495	2600	0.0016	-
0.3562	2650	0.0008	-
0.3629	2700	0.0003	-
0.3696	2750	0.0003	-
0.3763	2800	0.001	-
0.3831	2850	0.0003	-
0.3898	2900	0.0003	-
0.3965	2950	0.0003	-
0.4032	3000	0.0004	-
0.4099	3050	0.0002	-
0.4167	3100	0.0002	-
0.4234	3150	0.0001	-
0.4301	3200	0.0002	-
0.4368	3250	0.0001	-
0.4435	3300	0.0025	-
0.4503	3350	0.0011	-
0.4570	3400	0.0001	-
0.4637	3450	0.0002	-
0.4704	3500	0.0001	-
0.4772	3550	0.0001	-
0.4839	3600	0.0003	-
0.4906	3650	0.0001	-
0.4973	3700	0.0001	-
0.5040	3750	0.0002	-
0.5108	3800	0.0001	-
0.5175	3850	0.0	-
0.5242	3900	0.0001	-
0.5309	3950	0.0001	-
0.5376	4000	0.0002	-
0.5444	4050	0.0	-
0.5511	4100	0.0	-
0.5578	4150	0.0	-
0.5645	4200	0.0001	-
0.5712	4250	0.0001	-
0.5780	4300	0.0001	-
0.5847	4350	0.0001	-
0.5914	4400	0.0001	-
0.5981	4450	0.0001	-
0.6048	4500	0.0001	-
0.6116	4550	0.0002	-
0.6183	4600	0.0001	-
0.625	4650	0.0001	-
0.6317	4700	0.0001	-
0.6384	4750	0.0	-
0.6452	4800	0.0	-
0.6519	4850	0.0001	-
0.6586	4900	0.0	-
0.6653	4950	0.0	-
0.6720	5000	0.0	-
0.6788	5050	0.0001	-
0.6855	5100	0.0001	-
0.6922	5150	0.0	-
0.6989	5200	0.0001	-
0.7056	5250	0.0	-
0.7124	5300	0.0	-
0.7191	5350	0.0001	-
0.7258	5400	0.0001	-
0.7325	5450	0.0001	-
0.7392	5500	0.0	-
0.7460	5550	0.0001	-
0.7527	5600	0.0001	-
0.7594	5650	0.0001	-
0.7661	5700	0.0	-
0.7728	5750	0.0	-
0.7796	5800	0.0	-
0.7863	5850	0.0001	-
0.7930	5900	0.0	-
0.7997	5950	0.0	-
0.8065	6000	0.0	-
0.8132	6050	0.0	-
0.8199	6100	0.0	-
0.8266	6150	0.0	-
0.8333	6200	0.0001	-
0.8401	6250	0.0	-
0.8468	6300	0.0	-
0.8535	6350	0.0	-
0.8602	6400	0.0	-
0.8669	6450	0.0001	-
0.8737	6500	0.0	-
0.8804	6550	0.0	-
0.8871	6600	0.0001	-
0.8938	6650	0.0	-
0.9005	6700	0.0	-
0.9073	6750	0.0001	-
0.9140	6800	0.0059	-
0.9207	6850	0.0078	-
0.9274	6900	0.013	-
0.9341	6950	0.0001	-
0.9409	7000	0.0007	-
0.9476	7050	0.0001	-
0.9543	7100	0.0001	-
0.9610	7150	0.0001	-
0.9677	7200	0.0001	-
0.9745	7250	0.0001	-
0.9812	7300	0.0	-
0.9879	7350	0.0	-
0.9946	7400	0.0	-
1.0	7440	-	0.366
1.0013	7450	0.0001	-
1.0081	7500	0.0	-
1.0148	7550	0.0	-
1.0215	7600	0.0	-
1.0282	7650	0.0001	-
1.0349	7700	0.0001	-
1.0417	7750	0.0	-
1.0484	7800	0.0	-
1.0551	7850	0.0	-
1.0618	7900	0.0	-
1.0685	7950	0.0	-
1.0753	8000	0.0	-
1.0820	8050	0.0001	-
1.0887	8100	0.0	-
1.0954	8150	0.0	-
1.1022	8200	0.0	-
1.1089	8250	0.0	-
1.1156	8300	0.0	-
1.1223	8350	0.0	-
1.1290	8400	0.0	-
1.1358	8450	0.0	-
1.1425	8500	0.0001	-
1.1492	8550	0.0	-
1.1559	8600	0.0	-
1.1626	8650	0.0	-
1.1694	8700	0.0	-
1.1761	8750	0.0	-
1.1828	8800	0.0	-
1.1895	8850	0.0	-
1.1962	8900	0.0	-
1.2030	8950	0.0	-
1.2097	9000	0.0	-
1.2164	9050	0.0	-
1.2231	9100	0.0	-
1.2298	9150	0.0	-
1.2366	9200	0.0	-
1.2433	9250	0.0	-
1.25	9300	0.0	-
1.2567	9350	0.0	-
1.2634	9400	0.0	-
1.2702	9450	0.0	-
1.2769	9500	0.0	-
1.2836	9550	0.0	-
1.2903	9600	0.0	-
1.2970	9650	0.0	-
1.3038	9700	0.0	-
1.3105	9750	0.0	-
1.3172	9800	0.0	-
1.3239	9850	0.0	-
1.3306	9900	0.0	-
1.3374	9950	0.0	-
1.3441	10000	0.0	-
1.3508	10050	0.0	-
1.3575	10100	0.0	-
1.3642	10150	0.0	-
1.3710	10200	0.0	-
1.3777	10250	0.0	-
1.3844	10300	0.0	-
1.3911	10350	0.0	-
1.3978	10400	0.0	-
1.4046	10450	0.0	-
1.4113	10500	0.0	-
1.4180	10550	0.0	-
1.4247	10600	0.0	-
1.4315	10650	0.0	-
1.4382	10700	0.0	-
1.4449	10750	0.0	-
1.4516	10800	0.0	-
1.4583	10850	0.0	-
1.4651	10900	0.0	-
1.4718	10950	0.0	-
1.4785	11000	0.0	-
1.4852	11050	0.0	-
1.4919	11100	0.0	-
1.4987	11150	0.0	-
1.5054	11200	0.0	-
1.5121	11250	0.0	-
1.5188	11300	0.0	-
1.5255	11350	0.0	-
1.5323	11400	0.0	-
1.5390	11450	0.0	-
1.5457	11500	0.0	-
1.5524	11550	0.0	-
1.5591	11600	0.0	-
1.5659	11650	0.0	-
1.5726	11700	0.0	-
1.5793	11750	0.0	-
1.5860	11800	0.0	-
1.5927	11850	0.0	-
1.5995	11900	0.0	-
1.6062	11950	0.0	-
1.6129	12000	0.0	-
1.6196	12050	0.0	-
1.6263	12100	0.0	-
1.6331	12150	0.0	-
1.6398	12200	0.0	-
1.6465	12250	0.0	-
1.6532	12300	0.0	-
1.6599	12350	0.0	-
1.6667	12400	0.0	-
1.6734	12450	0.0	-
1.6801	12500	0.0	-
1.6868	12550	0.0	-
1.6935	12600	0.0	-
1.7003	12650	0.0	-
1.7070	12700	0.0	-
1.7137	12750	0.0	-
1.7204	12800	0.0	-
1.7272	12850	0.0	-
1.7339	12900	0.0	-
1.7406	12950	0.0	-
1.7473	13000	0.0	-
1.7540	13050	0.0	-
1.7608	13100	0.0	-
1.7675	13150	0.0	-
1.7742	13200	0.0	-
1.7809	13250	0.0	-
1.7876	13300	0.0	-
1.7944	13350	0.0	-
1.8011	13400	0.0	-
1.8078	13450	0.0	-
1.8145	13500	0.0	-
1.8212	13550	0.0	-
1.8280	13600	0.0	-
1.8347	13650	0.0	-
1.8414	13700	0.0	-
1.8481	13750	0.0	-
1.8548	13800	0.0	-
1.8616	13850	0.0	-
1.8683	13900	0.0	-
1.875	13950	0.0	-
1.8817	14000	0.0	-
1.8884	14050	0.0	-
1.8952	14100	0.0	-
1.9019	14150	0.0	-
1.9086	14200	0.0	-
1.9153	14250	0.0	-
1.9220	14300	0.0	-
1.9288	14350	0.0	-
1.9355	14400	0.0	-
1.9422	14450	0.0	-
1.9489	14500	0.0	-
1.9556	14550	0.0	-
1.9624	14600	0.0	-
1.9691	14650	0.0	-
1.9758	14700	0.0	-
1.9825	14750	0.0	-
1.9892	14800	0.0	-
1.9960	14850	0.0	-
2.0	14880	-	0.402
2.0027	14900	0.0	-
2.0094	14950	0.0	-
2.0161	15000	0.0	-
2.0228	15050	0.0	-
2.0296	15100	0.0	-
2.0363	15150	0.0	-
2.0430	15200	0.0	-
2.0497	15250	0.0	-
2.0565	15300	0.0	-
2.0632	15350	0.0	-
2.0699	15400	0.0	-
2.0766	15450	0.0	-
2.0833	15500	0.0	-
2.0901	15550	0.0	-
2.0968	15600	0.0	-
2.1035	15650	0.0	-
2.1102	15700	0.0	-
2.1169	15750	0.0	-
2.1237	15800	0.0	-
2.1304	15850	0.0	-
2.1371	15900	0.0	-
2.1438	15950	0.0	-
2.1505	16000	0.0	-
2.1573	16050	0.0	-
2.1640	16100	0.0	-
2.1707	16150	0.0	-
2.1774	16200	0.0	-
2.1841	16250	0.0	-
2.1909	16300	0.0	-
2.1976	16350	0.0	-
2.2043	16400	0.0	-
2.2110	16450	0.0	-
2.2177	16500	0.0	-
2.2245	16550	0.0	-
2.2312	16600	0.0	-
2.2379	16650	0.0	-
2.2446	16700	0.0	-
2.2513	16750	0.0	-
2.2581	16800	0.0	-
2.2648	16850	0.0	-
2.2715	16900	0.0	-
2.2782	16950	0.0	-
2.2849	17000	0.0	-
2.2917	17050	0.0	-
2.2984	17100	0.0	-
2.3051	17150	0.0	-
2.3118	17200	0.0	-
2.3185	17250	0.0	-
2.3253	17300	0.0	-
2.3320	17350	0.0	-
2.3387	17400	0.0	-
2.3454	17450	0.0	-
2.3522	17500	0.0	-
2.3589	17550	0.0	-
2.3656	17600	0.0	-
2.3723	17650	0.0	-
2.3790	17700	0.0	-
2.3858	17750	0.0	-
2.3925	17800	0.0	-
2.3992	17850	0.0	-
2.4059	17900	0.0	-
2.4126	17950	0.0	-
2.4194	18000	0.0	-
2.4261	18050	0.4962	-
2.4328	18100	0.2995	-
2.4395	18150	0.1101	-
2.4462	18200	0.0029	-
2.4530	18250	0.1094	-
2.4597	18300	0.0016	-
2.4664	18350	0.0003	-
2.4731	18400	0.0004	-
2.4798	18450	0.0001	-
2.4866	18500	0.0002	-
2.4933	18550	0.0002	-
2.5	18600	0.0002	-
2.5067	18650	0.0002	-
2.5134	18700	0.0001	-
2.5202	18750	0.0001	-
2.5269	18800	0.0	-
2.5336	18850	0.0003	-
2.5403	18900	0.0001	-
2.5470	18950	0.0001	-
2.5538	19000	0.0001	-
2.5605	19050	0.0	-
2.5672	19100	0.0001	-
2.5739	19150	0.0001	-
2.5806	19200	0.0001	-
2.5874	19250	0.0001	-
2.5941	19300	0.0001	-
2.6008	19350	0.0001	-
2.6075	19400	0.0001	-
2.6142	19450	0.0	-
2.6210	19500	0.0001	-
2.6277	19550	0.0001	-
2.6344	19600	0.0	-
2.6411	19650	0.0	-
2.6478	19700	0.0	-
2.6546	19750	0.0001	-
2.6613	19800	0.0	-
2.6680	19850	0.0	-
2.6747	19900	0.0	-
2.6815	19950	0.0	-
2.6882	20000	0.0	-
2.6949	20050	0.0	-
2.7016	20100	0.0	-
2.7083	20150	0.0	-
2.7151	20200	0.0	-
2.7218	20250	0.0	-
2.7285	20300	0.0	-
2.7352	20350	0.0	-
2.7419	20400	0.0	-
2.7487	20450	0.0	-
2.7554	20500	0.0	-
2.7621	20550	0.0	-
2.7688	20600	0.0	-
2.7755	20650	0.0	-
2.7823	20700	0.0	-
2.7890	20750	0.0	-
2.7957	20800	0.0	-
2.8024	20850	0.0	-
2.8091	20900	0.0	-
2.8159	20950	0.0	-
2.8226	21000	0.0	-
2.8293	21050	0.0	-
2.8360	21100	0.0	-
2.8427	21150	0.0	-
2.8495	21200	0.0	-
2.8562	21250	0.0	-
2.8629	21300	0.0	-
2.8696	21350	0.0	-
2.8763	21400	0.0	-
2.8831	21450	0.0	-
2.8898	21500	0.0	-
2.8965	21550	0.0	-
2.9032	21600	0.0	-
2.9099	21650	0.0	-
2.9167	21700	0.0	-
2.9234	21750	0.0	-
2.9301	21800	0.0	-
2.9368	21850	0.0	-
2.9435	21900	0.0	-
2.9503	21950	0.0	-
2.9570	22000	0.0	-
2.9637	22050	0.0	-
2.9704	22100	0.0	-
2.9772	22150	0.0	-
2.9839	22200	0.0001	-
2.9906	22250	0.0	-
2.9973	22300	0.0	-
3.0	22320	-	0.3872
3.0040	22350	0.0	-
3.0108	22400	0.0	-
3.0175	22450	0.0	-
3.0242	22500	0.0	-
3.0309	22550	0.0	-
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6.6667	49600	0.0	-
6.6734	49650	0.0	-
6.6801	49700	0.0	-
6.6868	49750	0.0	-
6.6935	49800	0.0	-
6.7003	49850	0.0	-
6.7070	49900	0.0	-
6.7137	49950	0.0	-
6.7204	50000	0.0	-
6.7272	50050	0.0	-
6.7339	50100	0.0	-
6.7406	50150	0.0	-
6.7473	50200	0.0	-
6.7540	50250	0.0	-
6.7608	50300	0.0	-
6.7675	50350	0.0	-
6.7742	50400	0.0	-
6.7809	50450	0.0	-
6.7876	50500	0.0	-
6.7944	50550	0.0	-
6.8011	50600	0.0	-
6.8078	50650	0.0	-
6.8145	50700	0.0	-
6.8212	50750	0.0	-
6.8280	50800	0.0	-
6.8347	50850	0.0	-
6.8414	50900	0.0	-
6.8481	50950	0.0	-
6.8548	51000	0.0	-
6.8616	51050	0.0	-
6.8683	51100	0.0	-
6.875	51150	0.0	-
6.8817	51200	0.0	-
6.8884	51250	0.0	-
6.8952	51300	0.0	-
6.9019	51350	0.0	-
6.9086	51400	0.0	-
6.9153	51450	0.0	-
6.9220	51500	0.0	-
6.9288	51550	0.0	-
6.9355	51600	0.0	-
6.9422	51650	0.0	-
6.9489	51700	0.0	-
6.9556	51750	0.0	-
6.9624	51800	0.0	-
6.9691	51850	0.0	-
6.9758	51900	0.0	-
6.9825	51950	0.0	-
6.9892	52000	0.0	-
6.9960	52050	0.0	-
7.0	52080	-	0.4134

• 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}
}