SetFit Aspect Model with sentence-transformers/all-MiniLM-L6-v2

This is a SetFit model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses sentence-transformers/all-MiniLM-L6-v2 as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification. In particular, this model is in charge of filtering aspect span candidates.

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.

This model was trained within the context of a larger system for ABSA, which looks like so:

Use a spaCy model to select possible aspect span candidates.
Use this SetFit model to filter these possible aspect span candidates.
Use a SetFit model to classify the filtered aspect span candidates.

Model Details

Model Description

Model Type: SetFit
Sentence Transformer body: sentence-transformers/all-MiniLM-L6-v2
Classification head: a LogisticRegression instance
spaCy Model: en_core_web_sm
SetFitABSA Aspect Model: marcelomoreno26/all-MiniLM-L6-v2-absa-aspect2
SetFitABSA Polarity Model: setfit-absa-polarity
Maximum Sequence Length: 256 tokens
Number of Classes: 2 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
aspect	'cord:I charge it at night and skip taking the cord with me because of the good battery life.' 'battery life:I charge it at night and skip taking the cord with me because of the good battery life.' 'service center:The tech guy then said the service center does not do 1-to-1 exchange and I have to direct my concern to the "sales" team, which is the retail shop which I bought my netbook from.'
no aspect	'night:I charge it at night and skip taking the cord with me because of the good battery life.' 'skip:I charge it at night and skip taking the cord with me because of the good battery life.' 'exchange:The tech guy then said the service center does not do 1-to-1 exchange and I have to direct my concern to the "sales" team, which is the retail shop which I bought my netbook from.'

Evaluation

Metrics

Label	Accuracy
all	0.8948

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 AbsaModel

# Download from the 🤗 Hub
model = AbsaModel.from_pretrained(
    "marcelomoreno26/all-MiniLM-L6-v2-absa-aspect2",
    "setfit-absa-polarity",
)
# Run inference
preds = model("The food was great, but the venue is just way too busy.")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	2	21.9670	75

Label	Training Sample Count
no aspect	690
aspect	644

Training Hyperparameters

batch_size: (16, 2)
num_epochs: (1, 16)
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: False

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0000	1	0.3662	-
0.0015	50	0.3374	-
0.0029	100	0.3411	-
0.0044	150	0.2945	-
0.0059	200	0.2944	-
0.0073	250	0.2942	-
0.0088	300	0.2409	-
0.0103	350	0.2817	-
0.0118	400	0.3149	-
0.0132	450	0.2618	-
0.0147	500	0.247	-
0.0162	550	0.2883	-
0.0176	600	0.2783	-
0.0191	650	0.2418	-
0.0206	700	0.2938	-
0.0220	750	0.2376	-
0.0235	800	0.2652	-
0.0250	850	0.2442	-
0.0265	900	0.2678	-
0.0279	950	0.2216	-
0.0294	1000	0.1816	-
0.0309	1050	0.1102	-
0.0323	1100	0.2985	-
0.0338	1150	0.1124	-
0.0353	1200	0.1075	-
0.0367	1250	0.0819	-
0.0382	1300	0.1238	-
0.0397	1350	0.0529	-
0.0412	1400	0.026	-
0.0426	1450	0.0289	-
0.0441	1500	0.067	-
0.0456	1550	0.0276	-
0.0470	1600	0.0162	-
0.0485	1650	0.0083	-
0.0500	1700	0.0017	-
0.0514	1750	0.0028	-
0.0529	1800	0.0045	-
0.0544	1850	0.0022	-
0.0558	1900	0.0014	-
0.0573	1950	0.0059	-
0.0588	2000	0.0019	-
0.0603	2050	0.0014	-
0.0617	2100	0.0022	-
0.0632	2150	0.0005	-
0.0647	2200	0.0008	-
0.0661	2250	0.0005	-
0.0676	2300	0.0006	-
0.0691	2350	0.0003	-
0.0705	2400	0.0007	-
0.0720	2450	0.0005	-
0.0735	2500	0.0005	-
0.0750	2550	0.0612	-
0.0764	2600	0.0004	-
0.0779	2650	0.041	-
0.0794	2700	0.0002	-
0.0808	2750	0.0003	-
0.0823	2800	0.0002	-
0.0838	2850	0.0002	-
0.0852	2900	0.0002	-
0.0867	2950	0.0004	-
0.0882	3000	0.0006	-
0.0897	3050	0.0601	-
0.0911	3100	0.0002	-
0.0926	3150	0.0108	-
0.0941	3200	0.0003	-
0.0955	3250	0.0363	-
0.0970	3300	0.0006	-
0.0985	3350	0.0002	-
0.0999	3400	0.0033	-
0.1014	3450	0.0002	-
0.1029	3500	0.0002	-
0.1044	3550	0.0006	-
0.1058	3600	0.0002	-
0.1073	3650	0.0002	-
0.1088	3700	0.0001	-
0.1102	3750	0.0002	-
0.1117	3800	0.0002	-
0.1132	3850	0.0004	-
0.1146	3900	0.0003	-
0.1161	3950	0.0001	-
0.1176	4000	0.0004	-
0.1190	4050	0.0003	-
0.1205	4100	0.001	-
0.1220	4150	0.0002	-
0.1235	4200	0.0001	-
0.1249	4250	0.0003	-
0.1264	4300	0.0003	-
0.1279	4350	0.0002	-
0.1293	4400	0.0001	-
0.1308	4450	0.0001	-
0.1323	4500	0.0001	-
0.1337	4550	0.0001	-
0.1352	4600	0.0001	-
0.1367	4650	0.0003	-
0.1382	4700	0.0006	-
0.1396	4750	0.0003	-
0.1411	4800	0.0001	-
0.1426	4850	0.0011	-
0.1440	4900	0.0001	-
0.1455	4950	0.0001	-
0.1470	5000	0.0001	-
0.1484	5050	0.0001	-
0.1499	5100	0.0002	-
0.1514	5150	0.0497	-
0.1529	5200	0.0002	-
0.1543	5250	0.0001	-
0.1558	5300	0.0008	-
0.1573	5350	0.0001	-
0.1587	5400	0.0002	-
0.1602	5450	0.0001	-
0.1617	5500	0.0003	-
0.1631	5550	0.0003	-
0.1646	5600	0.0004	-
0.1661	5650	0.0002	-
0.1675	5700	0.0002	-
0.1690	5750	0.0001	-
0.1705	5800	0.0001	-
0.1720	5850	0.0001	-
0.1734	5900	0.0004	-
0.1749	5950	0.0001	-
0.1764	6000	0.0001	-
0.1778	6050	0.0001	-
0.125	1	0.0002	-
0.5	4	0.0003	-
1.0	8	0.0	-
0.0000	1	0.0001	-
0.0015	50	0.0001	-
0.0029	100	0.0	-
0.0044	150	0.0001	-
0.125	1	0.0	-
0.5	4	0.0	-
0.0000	1	0.0003	-
0.0009	50	0.0003	-
0.0018	100	0.0003	-
0.0027	150	0.0001	-
0.0036	200	0.0001	-
0.0045	250	0.1015	-
0.0054	300	0.0005	-
0.0063	350	0.0579	-
0.0072	400	0.0001	-
0.0081	450	0.0897	-
0.0090	500	0.0618	-
0.0099	550	0.0002	-
0.0108	600	0.0001	-
0.0117	650	0.0004	-
0.0126	700	0.0002	-
0.0135	750	0.0002	-
0.0143	800	0.0001	-
0.0152	850	0.062	-
0.0161	900	0.0004	-
0.0170	950	0.0002	-
0.0179	1000	0.0001	-
0.0188	1050	0.0628	-
0.0197	1100	0.0003	-
0.0206	1150	0.0003	-
0.0215	1200	0.0001	-
0.0224	1250	0.0001	-
0.0233	1300	0.0001	-
0.0000	1	0.0002	-
0.0009	50	0.0002	-
0.0018	100	0.0001	-
0.0027	150	0.0001	-
0.0036	200	0.0001	-
0.0045	250	0.0002	-
0.0054	300	0.0001	-
0.0063	350	0.0002	-
0.0072	400	0.0002	-
0.0081	450	0.0262	-
0.0090	500	0.0001	-
0.0099	550	0.0002	-
0.0108	600	0.0001	-
0.0117	650	0.0001	-
0.0126	700	0.0001	-
0.0135	750	0.0001	-
0.0143	800	0.0001	-
0.0152	850	0.0002	-
0.0161	900	0.0001	-
0.0170	950	0.0001	-
0.0179	1000	0.0001	-
0.0188	1050	0.06	-
0.0197	1100	0.0001	-
0.0206	1150	0.0001	-
0.0215	1200	0.0001	-
0.0224	1250	0.0001	-
0.0233	1300	0.0001	-
0.0242	1350	0.0001	-
0.0251	1400	0.0001	-
0.0260	1450	0.0001	-
0.0269	1500	0.0002	-
0.0278	1550	0.0001	-
0.0287	1600	0.0001	-
0.0296	1650	0.0125	-
0.0305	1700	0.0001	-
0.0314	1750	0.0001	-
0.0323	1800	0.0001	-
0.0332	1850	0.0001	-
0.0341	1900	0.0001	-
0.0350	1950	0.0001	-
0.0359	2000	0.0001	-
0.0368	2050	0.0001	-
0.0377	2100	0.0002	-
0.0386	2150	0.0001	-
0.0395	2200	0.0001	-
0.0404	2250	0.0407	-
0.0412	2300	0.0001	-
0.0421	2350	0.0001	-
0.0430	2400	0.0001	-
0.0439	2450	0.0001	-
0.0448	2500	0.0001	-
0.0457	2550	0.0	-
0.0466	2600	0.0	-
0.0475	2650	0.0001	-
0.0484	2700	0.0	-
0.0493	2750	0.0001	-
0.0502	2800	0.0001	-
0.0511	2850	0.0001	-
0.0520	2900	0.0001	-
0.0529	2950	0.0002	-
0.0538	3000	0.0001	-
0.0547	3050	0.0001	-
0.0556	3100	0.0001	-
0.0565	3150	0.0001	-
0.0574	3200	0.0	-
0.0583	3250	0.0	-
0.0592	3300	0.0	-
0.0601	3350	0.0001	-
0.0610	3400	0.0	-
0.0619	3450	0.0	-
0.0628	3500	0.0001	-
0.0637	3550	0.0001	-
0.0646	3600	0.0	-
0.0655	3650	0.0001	-
0.0664	3700	0.0	-
0.0673	3750	0.0001	-
0.0681	3800	0.0	-
0.0690	3850	0.0005	-
0.0699	3900	0.0001	-
0.0708	3950	0.0001	-
0.0717	4000	0.0	-
0.0726	4050	0.0001	-
0.0735	4100	0.0009	-
0.0744	4150	0.0001	-
0.0753	4200	0.0001	-
0.0762	4250	0.0001	-
0.0771	4300	0.0	-
0.0780	4350	0.0001	-
0.0789	4400	0.0001	-
0.0798	4450	0.0001	-
0.0807	4500	0.0	-
0.0816	4550	0.0	-
0.0825	4600	0.0001	-
0.0834	4650	0.0	-
0.0843	4700	0.0	-
0.0852	4750	0.0	-
0.0861	4800	0.0	-
0.0870	4850	0.0	-
0.0879	4900	0.0004	-
0.0888	4950	0.0002	-
0.0897	5000	0.0001	-
0.0906	5050	0.0001	-
0.0915	5100	0.0	-
0.0924	5150	0.0026	-
0.0933	5200	0.0549	-
0.0942	5250	0.0001	-
0.0950	5300	0.0011	-
0.0959	5350	0.0	-
0.0968	5400	0.0	-
0.0977	5450	0.0	-
0.0986	5500	0.0002	-
0.0995	5550	0.0001	-
0.1004	5600	0.0	-
0.1013	5650	0.0001	-
0.1022	5700	0.0001	-
0.1031	5750	0.0	-
0.1040	5800	0.0	-
0.1049	5850	0.0	-
0.1058	5900	0.0203	-
0.1067	5950	0.0001	-
0.1076	6000	0.0	-
0.1085	6050	0.0	-
0.1094	6100	0.0	-
0.1103	6150	0.0	-
0.1112	6200	0.0001	-
0.1121	6250	0.0	-
0.1130	6300	0.0	-
0.1139	6350	0.0	-
0.1148	6400	0.0	-
0.1157	6450	0.0164	-
0.1166	6500	0.0001	-
0.1175	6550	0.0	-
0.1184	6600	0.0001	-
0.1193	6650	0.0002	-
0.1202	6700	0.0001	-
0.1211	6750	0.0	-
0.1219	6800	0.0	-
0.1228	6850	0.0	-
0.1237	6900	0.0	-
0.1246	6950	0.0	-
0.1255	7000	0.0001	-
0.1264	7050	0.0	-
0.1273	7100	0.0	-
0.1282	7150	0.0	-
0.1291	7200	0.0002	-
0.1300	7250	0.0	-
0.1309	7300	0.0	-
0.1318	7350	0.0	-
0.1327	7400	0.0	-
0.1336	7450	0.0	-
0.1345	7500	0.0002	-
0.1354	7550	0.0	-
0.1363	7600	0.0	-
0.1372	7650	0.0001	-
0.1381	7700	0.0001	-
0.1390	7750	0.0001	-
0.1399	7800	0.0001	-
0.1408	7850	0.0	-
0.1417	7900	0.0	-
0.1426	7950	0.0	-
0.1435	8000	0.0142	-
0.1444	8050	0.0001	-
0.1453	8100	0.0	-
0.1462	8150	0.0002	-
0.1471	8200	0.0	-
0.1480	8250	0.0	-
0.1488	8300	0.0	-
0.1497	8350	0.0	-
0.1506	8400	0.0003	-
0.1515	8450	0.0	-
0.1524	8500	0.0	-
0.1533	8550	0.0	-
0.1542	8600	0.0	-
0.1551	8650	0.0	-
0.1560	8700	0.0	-
0.1569	8750	0.0	-
0.1578	8800	0.0	-
0.1587	8850	0.0	-
0.1596	8900	0.0	-
0.1605	8950	0.0	-
0.1614	9000	0.0	-
0.1623	9050	0.0	-
0.1632	9100	0.0	-
0.1641	9150	0.0	-
0.1650	9200	0.0	-
0.1659	9250	0.0001	-
0.1668	9300	0.0	-
0.1677	9350	0.0	-
0.1686	9400	0.0	-
0.1695	9450	0.0	-
0.1704	9500	0.0	-
0.1713	9550	0.0	-
0.1722	9600	0.0	-
0.1731	9650	0.0	-
0.1740	9700	0.0	-
0.1749	9750	0.0	-
0.1758	9800	0.0	-
0.1766	9850	0.0	-
0.1775	9900	0.0	-
0.1784	9950	0.0	-
0.1793	10000	0.0	-
0.1802	10050	0.0097	-
0.1811	10100	0.0	-
0.1820	10150	0.0	-
0.1829	10200	0.0	-
0.1838	10250	0.0	-
0.1847	10300	0.0001	-
0.1856	10350	0.0	-
0.1865	10400	0.0	-
0.1874	10450	0.0	-
0.1883	10500	0.0	-
0.1892	10550	0.0	-
0.1901	10600	0.0	-
0.1910	10650	0.0	-
0.1919	10700	0.0	-
0.1928	10750	0.0	-
0.1937	10800	0.0	-
0.1946	10850	0.0	-
0.1955	10900	0.0	-
0.1964	10950	0.0	-
0.1973	11000	0.0001	-
0.1982	11050	0.0	-
0.1991	11100	0.0	-
0.2000	11150	0.0	-
0.2009	11200	0.0	-
0.2018	11250	0.0004	-
0.2027	11300	0.0001	-
0.2035	11350	0.0001	-
0.2044	11400	0.0	-
0.2053	11450	0.0001	-
0.2062	11500	0.0	-
0.2071	11550	0.0001	-
0.2080	11600	0.0	-
0.2089	11650	0.0	-
0.2098	11700	0.0	-
0.2107	11750	0.0	-
0.2116	11800	0.0	-
0.2125	11850	0.0	-
0.2134	11900	0.0	-
0.2143	11950	0.0001	-
0.2152	12000	0.0	-
0.2161	12050	0.0	-
0.2170	12100	0.0	-
0.2179	12150	0.0	-
0.2188	12200	0.0	-
0.2197	12250	0.0	-
0.2206	12300	0.0	-
0.2215	12350	0.0	-
0.2224	12400	0.0	-
0.2233	12450	0.0	-
0.2242	12500	0.0	-
0.2251	12550	0.0	-
0.2260	12600	0.0	-
0.2269	12650	0.0	-
0.2278	12700	0.0	-
0.2287	12750	0.0	-
0.2296	12800	0.0	-
0.2304	12850	0.0	-
0.2313	12900	0.0	-
0.2322	12950	0.0	-
0.2331	13000	0.0	-
0.2340	13050	0.0	-
0.2349	13100	0.0	-
0.2358	13150	0.0264	-
0.2367	13200	0.0	-
0.2376	13250	0.0	-
0.2385	13300	0.0	-
0.2394	13350	0.0	-
0.2403	13400	0.0	-
0.2412	13450	0.0	-
0.2421	13500	0.0	-
0.2430	13550	0.0	-
0.2439	13600	0.0	-
0.2448	13650	0.0	-
0.2457	13700	0.0	-
0.2466	13750	0.0	-
0.2475	13800	0.0	-
0.2484	13850	0.0	-
0.2493	13900	0.0	-
0.2502	13950	0.0	-
0.2511	14000	0.0	-
0.2520	14050	0.0	-
0.2529	14100	0.0	-
0.2538	14150	0.0001	-
0.2547	14200	0.0	-
0.2556	14250	0.0	-
0.2565	14300	0.0	-
0.2573	14350	0.0	-
0.2582	14400	0.0	-
0.2591	14450	0.0	-
0.2600	14500	0.0	-
0.2609	14550	0.0001	-
0.2618	14600	0.0	-
0.2627	14650	0.0	-
0.2636	14700	0.0	-
0.2645	14750	0.0	-
0.2654	14800	0.0	-
0.2663	14850	0.0	-
0.2672	14900	0.0	-
0.2681	14950	0.0001	-
0.2690	15000	0.0	-
0.2699	15050	0.0	-
0.2708	15100	0.0	-
0.2717	15150	0.0	-
0.2726	15200	0.0	-
0.2735	15250	0.0	-
0.2744	15300	0.0	-
0.2753	15350	0.0	-
0.2762	15400	0.0	-
0.2771	15450	0.0	-
0.2780	15500	0.0001	-
0.2789	15550	0.0621	-
0.2798	15600	0.0056	-
0.2807	15650	0.0	-
0.2816	15700	0.0	-
0.2825	15750	0.0145	-
0.2834	15800	0.0	-
0.2842	15850	0.0	-
0.2851	15900	0.0166	-
0.2860	15950	0.0	-
0.2869	16000	0.0	-
0.2878	16050	0.0	-
0.2887	16100	0.0166	-
0.2896	16150	0.0	-
0.2905	16200	0.0	-
0.2914	16250	0.0169	-
0.2923	16300	0.0	-
0.2932	16350	0.0	-
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Framework Versions

Python: 3.10.12
SetFit: 1.0.3
Sentence Transformers: 2.7.0
spaCy: 3.7.4
Transformers: 4.40.1
PyTorch: 2.2.1+cu121
Datasets: 2.19.0
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}
}

marcelomoreno26
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all-MiniLM-L6-v2-absa-aspect