metadata

library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
metrics:
  - accuracy
widget:
  - text: Dadon Hotel
  - text: Joyi Homeo Hall
  - text: Masum Egg Supplier
  - text: Salam Automobiles
  - text: Shoumik Enterprise
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/paraphrase-mpnet-base-v2
model-index:
  - name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 0.59
            name: Accuracy

SetFit with sentence-transformers/paraphrase-mpnet-base-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-mpnet-base-v2 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:

Fine-tuning a Sentence Transformer with contrastive learning.
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-mpnet-base-v2
Classification head: a LogisticRegression instance
Maximum Sequence Length: 512 tokens
Number of Classes: 19 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
Landmark	'Rajarbagh Shahjahanpur Moar' '3 Number Mp Checkpost' 'Akua Madrasha Quarter Rail Crossing'
Religious Place	'Csd Masjid Jashore Cantonment' 'Baytut Tayyib Jame Masjid' 'Court Para Jame Masjid'
Residential	'Matrichaya' 'Chowdhury Mansion' 'Nabila Villa'
Government	'Rapid Action Battalion 3 (RAB)' 'Bangladesh Muktijoddha Sangsad Tejgaon Industrial Area Unit Command' 'Subhanighat Police Outpost'
Recreation	'5h Avenue Convention Hall' 'Purobi Chinema Hall' 'Shotodol Club'
Commercial	'Bikers Racing Zone' 'Sr Parcel Service Limited' 'Vegetables Market'
Education	'Dinajpur Polytechnic Institute' 'Gazipur Central Public Management Institute' 'Maskatadighi Government Primary School'
Bank	'Commercial Bank Of Ceylon Plc Shantinagar' 'Social Islami Bank Limited (SIBL) Mohammadpur' 'Dhaka Bank Limited Jashore Sadar'
Fuel	'Ferdaous Enterprise' 'Ahmed Bd Trade' 'Hasan Filling Station'
Hotel	'Hotel Arafat' 'World Beach Resort' 'Hotel Chalontika'
Shop	'Janosheba Medico' 'New Mone Rekho Tailors' 'Man Tailors'
Utility	'Pole No 86' 'Pole No 239' 'Salimullah Road Water Pump'
Construction	'N J Enterprise' 'Bk Traders' 'Sohel Glass'
Agricultural	'Mango Seedlings' 'Amaia Garden' 'Gardey'
Office	'Dalik Lekhak And Serviour' 'Assistant Police Commissioner Traffic Mohammadpur Zone Office' 'Challenger Elevator And Escalator Limited'
Healthcare	'Afroz Homeo Hall' 'Expert Dental Solution' 'Bhai Bon Homeo Hall'
Transportation	'Ideal Parking Jone' 'Hanif Bus Counter' 'Five Star Transport Agency'
Food	'Madhuban Hotel' 'Al Hamza Biriyani House' 'Gramer Bari'
Industry	'Rony Group' 'Net Factory' 'Kumudini Welfare Trust Of Bengal Bd Limited'

Evaluation

Metrics

Label	Accuracy
all	0.59

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("rafi138/setfit-paraphrase-mpnet-base-v2-business-type")
# Run inference
preds = model("Dadon Hotel")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	1	3.3752	11

Label	Training Sample Count
ShopCommercialGovernmentHealthcareEducationFoodOfficeReligious PlaceBankTransportationConstructionIndustryResidentialLandmarkRecreationFuelHotelUtilityAgricultural	0

Training Hyperparameters

batch_size: (64, 64)
num_epochs: (4, 4)
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	0.2815	-
0.0019	50	0.2543	-
0.0038	100	0.2781	-
0.0057	150	0.2328	-
0.0076	200	0.2258	-
0.0095	250	0.1884	-
0.0115	300	0.2192	-
0.0134	350	0.1935	-
0.0153	400	0.2057	-
0.0172	450	0.1949	-
0.0191	500	0.1955	-
0.0210	550	0.1744	-
0.0229	600	0.1518	-
0.0248	650	0.1804	-
0.0267	700	0.1669	-
0.0286	750	0.1565	-
0.0306	800	0.14	-
0.0325	850	0.1648	-
0.0344	900	0.1441	-
0.0363	950	0.1237	-
0.0382	1000	0.1155	-
0.0401	1050	0.1303	-
0.0420	1100	0.1115	-
0.0439	1150	0.1533	-
0.0458	1200	0.1278	-
0.0477	1250	0.0851	-
0.0496	1300	0.0982	-
0.0516	1350	0.0925	-
0.0535	1400	0.0861	-
0.0554	1450	0.1121	-
0.0573	1500	0.1226	-
0.0592	1550	0.1049	-
0.0611	1600	0.1101	-
0.0630	1650	0.113	-
0.0649	1700	0.1061	-
0.0668	1750	0.0573	-
0.0687	1800	0.0665	-
0.0707	1850	0.0657	-
0.0726	1900	0.082	-
0.0745	1950	0.0654	-
0.0764	2000	0.0783	-
0.0783	2050	0.0713	-
0.0802	2100	0.0669	-
0.0821	2150	0.0536	-
0.0840	2200	0.0556	-
0.0859	2250	0.0704	-
0.0878	2300	0.063	-
0.0897	2350	0.0472	-
0.0917	2400	0.0562	-
0.0936	2450	0.0392	-
0.0955	2500	0.0465	-
0.0974	2550	0.0424	-
0.0993	2600	0.0453	-
0.1012	2650	0.0375	-
0.1031	2700	0.0445	-
0.1050	2750	0.0231	-
0.1069	2800	0.0386	-
0.1088	2850	0.0258	-
0.1108	2900	0.0344	-
0.1127	2950	0.0143	-
0.1146	3000	0.016	-
0.1165	3050	0.0216	-
0.1184	3100	0.0365	-
0.1203	3150	0.0238	-
0.1222	3200	0.0099	-
0.1241	3250	0.0211	-
0.1260	3300	0.0149	-
0.1279	3350	0.0406	-
0.1298	3400	0.0231	-
0.1318	3450	0.0266	-
0.1337	3500	0.0225	-
0.1356	3550	0.0203	-
0.1375	3600	0.0097	-
0.1394	3650	0.0143	-
0.1413	3700	0.0052	-
0.1432	3750	0.0094	-
0.1451	3800	0.0029	-
0.1470	3850	0.0065	-
0.1489	3900	0.0078	-
0.1508	3950	0.0109	-
0.1528	4000	0.0054	-
0.1547	4050	0.0069	-
0.1566	4100	0.0248	-
0.1585	4150	0.0069	-
0.1604	4200	0.0073	-
0.1623	4250	0.0054	-
0.1642	4300	0.0027	-
0.1661	4350	0.0028	-
0.1680	4400	0.0021	-
0.1699	4450	0.0038	-
0.1719	4500	0.0034	-
0.1738	4550	0.0158	-
0.1757	4600	0.0043	-
0.1776	4650	0.0055	-
0.1795	4700	0.0064	-
0.1814	4750	0.0021	-
0.1833	4800	0.0026	-
0.1852	4850	0.0112	-
0.1871	4900	0.0029	-
0.1890	4950	0.0176	-
0.1909	5000	0.0071	-
0.1929	5050	0.0097	-
0.1948	5100	0.0032	-
0.1967	5150	0.0044	-
0.1986	5200	0.0065	-
0.2005	5250	0.0176	-
0.2024	5300	0.0026	-
0.2043	5350	0.0036	-
0.2062	5400	0.0022	-
0.2081	5450	0.0014	-
0.2100	5500	0.0073	-
0.2120	5550	0.0009	-
0.2139	5600	0.0011	-
0.2158	5650	0.0022	-
0.2177	5700	0.0011	-
0.2196	5750	0.0074	-
0.2215	5800	0.0114	-
0.2234	5850	0.0013	-
0.2253	5900	0.0009	-
0.2272	5950	0.0018	-
0.2291	6000	0.001	-
0.2310	6050	0.0013	-
0.2330	6100	0.0058	-
0.2349	6150	0.0012	-
0.2368	6200	0.0013	-
0.2387	6250	0.0009	-
0.2406	6300	0.0012	-
0.2425	6350	0.0006	-
0.2444	6400	0.0013	-
0.2463	6450	0.0005	-
0.2482	6500	0.0032	-
0.2501	6550	0.0006	-
0.2521	6600	0.0007	-
0.2540	6650	0.0029	-
0.2559	6700	0.001	-
0.2578	6750	0.0005	-
0.2597	6800	0.001	-
0.2616	6850	0.0004	-
0.2635	6900	0.0007	-
0.2654	6950	0.0006	-
0.2673	7000	0.0034	-
0.2692	7050	0.0006	-
0.2711	7100	0.0006	-
0.2731	7150	0.0013	-
0.2750	7200	0.0017	-
0.2769	7250	0.0006	-
0.2788	7300	0.0007	-
0.2807	7350	0.0005	-
0.2826	7400	0.0009	-
0.2845	7450	0.0007	-
0.2864	7500	0.0008	-
0.2883	7550	0.001	-
0.2902	7600	0.0004	-
0.2922	7650	0.0004	-
0.2941	7700	0.0007	-
0.2960	7750	0.0004	-
0.2979	7800	0.0011	-
0.2998	7850	0.0005	-
0.3017	7900	0.0004	-
0.3036	7950	0.0003	-
0.3055	8000	0.001	-
0.3074	8050	0.0005	-
0.3093	8100	0.0003	-
0.3112	8150	0.0004	-
0.3132	8200	0.0003	-
0.3151	8250	0.007	-
0.3170	8300	0.0026	-
0.3189	8350	0.0004	-
0.3208	8400	0.0005	-
0.3227	8450	0.0005	-
0.3246	8500	0.0004	-
0.3265	8550	0.0003	-
0.3284	8600	0.0003	-
0.3303	8650	0.0003	-
0.3323	8700	0.0026	-
0.3342	8750	0.0003	-
0.3361	8800	0.0002	-
0.3380	8850	0.0002	-
0.3399	8900	0.0002	-
0.3418	8950	0.0004	-
0.3437	9000	0.0003	-
0.3456	9050	0.0005	-
0.3475	9100	0.0002	-
0.3494	9150	0.0052	-
0.3513	9200	0.0138	-
0.3533	9250	0.0006	-
0.3552	9300	0.0004	-
0.3571	9350	0.0019	-
0.3590	9400	0.0003	-
0.3609	9450	0.0002	-
0.3628	9500	0.0002	-
0.3647	9550	0.0003	-
0.3666	9600	0.0003	-
0.3685	9650	0.0269	-
0.3704	9700	0.0004	-
0.3724	9750	0.0002	-
0.3743	9800	0.0005	-
0.3762	9850	0.0002	-
0.3781	9900	0.0003	-
0.3800	9950	0.0002	-
0.3819	10000	0.0002	-
0.3838	10050	0.0002	-
0.3857	10100	0.0158	-
0.3876	10150	0.0004	-
0.3895	10200	0.0094	-
0.3914	10250	0.0098	-
0.3934	10300	0.0003	-
0.3953	10350	0.0003	-
0.3972	10400	0.0004	-
0.3991	10450	0.0003	-
0.4010	10500	0.0003	-
0.4029	10550	0.0002	-
0.4048	10600	0.0006	-
0.4067	10650	0.0004	-
0.4086	10700	0.0002	-
0.4105	10750	0.0005	-
0.4124	10800	0.0002	-
0.4144	10850	0.0002	-
0.4163	10900	0.0002	-
0.4182	10950	0.0002	-
0.4201	11000	0.0002	-
0.4220	11050	0.0002	-
0.4239	11100	0.0002	-
0.4258	11150	0.0002	-
0.4277	11200	0.0002	-
0.4296	11250	0.0002	-
0.4315	11300	0.0001	-
0.4335	11350	0.0001	-
0.4354	11400	0.0001	-
0.4373	11450	0.0002	-
0.4392	11500	0.0001	-
0.4411	11550	0.0002	-
0.4430	11600	0.0001	-
0.4449	11650	0.0001	-
0.4468	11700	0.0002	-
0.4487	11750	0.0001	-
0.4506	11800	0.0001	-
0.4525	11850	0.0002	-
0.4545	11900	0.0006	-
0.4564	11950	0.0002	-
0.4583	12000	0.0001	-
0.4602	12050	0.0001	-
0.4621	12100	0.0002	-
0.4640	12150	0.0002	-
0.4659	12200	0.0001	-
0.4678	12250	0.0001	-
0.4697	12300	0.0002	-
0.4716	12350	0.0004	-
0.4736	12400	0.0003	-
0.4755	12450	0.0279	-
0.4774	12500	0.0072	-
0.4793	12550	0.006	-
0.4812	12600	0.0325	-
0.4831	12650	0.0012	-
0.4850	12700	0.0015	-
0.4869	12750	0.0013	-
0.4888	12800	0.0002	-
0.4907	12850	0.0003	-
0.4926	12900	0.0003	-
0.4946	12950	0.001	-
0.4965	13000	0.0004	-
0.4984	13050	0.0002	-
0.5003	13100	0.0002	-
0.5022	13150	0.0002	-
0.5041	13200	0.0003	-
0.5060	13250	0.0001	-
0.5079	13300	0.0001	-
0.5098	13350	0.0002	-
0.5117	13400	0.0001	-
0.5137	13450	0.0002	-
0.5156	13500	0.0001	-
0.5175	13550	0.0002	-
0.5194	13600	0.0002	-
0.5213	13650	0.0002	-
0.5232	13700	0.0001	-
0.5251	13750	0.0002	-
0.5270	13800	0.0002	-
0.5289	13850	0.0001	-
0.5308	13900	0.0001	-
0.5327	13950	0.0002	-
0.5347	14000	0.0001	-
0.5366	14050	0.0001	-
0.5385	14100	0.0001	-
0.5404	14150	0.0001	-
0.5423	14200	0.0149	-
0.5442	14250	0.0002	-
0.5461	14300	0.0009	-
0.5480	14350	0.0057	-
0.5499	14400	0.0089	-
0.5518	14450	0.0154	-
0.5538	14500	0.0003	-
0.5557	14550	0.0016	-
0.5576	14600	0.0001	-
0.5595	14650	0.0001	-
0.5614	14700	0.0002	-
0.5633	14750	0.0002	-
0.5652	14800	0.0004	-
0.5671	14850	0.0004	-
0.5690	14900	0.0004	-
0.5709	14950	0.0002	-
0.5728	15000	0.0001	-
0.5748	15050	0.0001	-
0.5767	15100	0.0002	-
0.5786	15150	0.0002	-
0.5805	15200	0.0001	-
0.5824	15250	0.0002	-
0.5843	15300	0.0001	-
0.5862	15350	0.0002	-
0.5881	15400	0.0001	-
0.5900	15450	0.0113	-
0.5919	15500	0.0002	-
0.5939	15550	0.0002	-
0.5958	15600	0.0001	-
0.5977	15650	0.0001	-
0.5996	15700	0.0004	-
0.6015	15750	0.0009	-
0.6034	15800	0.001	-
0.6053	15850	0.0002	-
0.6072	15900	0.0002	-
0.6091	15950	0.0003	-
0.6110	16000	0.0002	-
0.6129	16050	0.0004	-
0.6149	16100	0.0001	-
0.6168	16150	0.0001	-
0.6187	16200	0.0001	-
0.6206	16250	0.0001	-
0.6225	16300	0.0001	-
0.6244	16350	0.0001	-
0.6263	16400	0.0001	-
0.6282	16450	0.0008	-
0.6301	16500	0.0001	-
0.6320	16550	0.0001	-
0.6340	16600	0.0001	-
0.6359	16650	0.0001	-
0.6378	16700	0.0001	-
0.6397	16750	0.0001	-
0.6416	16800	0.0001	-
0.6435	16850	0.0004	-
0.6454	16900	0.0001	-
0.6473	16950	0.0001	-
0.6492	17000	0.0001	-
0.6511	17050	0.0001	-
0.6530	17100	0.0001	-
0.6550	17150	0.0003	-
0.6569	17200	0.0003	-
0.6588	17250	0.0002	-
0.6607	17300	0.0002	-
0.6626	17350	0.0011	-
0.6645	17400	0.0003	-
0.6664	17450	0.0001	-
0.6683	17500	0.0003	-
0.6702	17550	0.0001	-
0.6721	17600	0.0002	-
0.6741	17650	0.0001	-
0.6760	17700	0.0001	-
0.6779	17750	0.0001	-
0.6798	17800	0.0001	-
0.6817	17850	0.0001	-
0.6836	17900	0.0001	-
0.6855	17950	0.0001	-
0.6874	18000	0.0001	-
0.6893	18050	0.0001	-
0.6912	18100	0.0001	-
0.6931	18150	0.0023	-
0.6951	18200	0.0001	-
0.6970	18250	0.0001	-
0.6989	18300	0.0001	-
0.7008	18350	0.0001	-
0.7027	18400	0.0001	-
0.7046	18450	0.0001	-
0.7065	18500	0.0001	-
0.7084	18550	0.0001	-
0.7103	18600	0.0001	-
0.7122	18650	0.0001	-
0.7141	18700	0.0001	-
0.7161	18750	0.0001	-
0.7180	18800	0.0001	-
0.7199	18850	0.0001	-
0.7218	18900	0.0001	-
0.7237	18950	0.0001	-
0.7256	19000	0.0001	-
0.7275	19050	0.0001	-
0.7294	19100	0.0104	-
0.7313	19150	0.0041	-
0.7332	19200	0.0045	-
0.7352	19250	0.0002	-
0.7371	19300	0.0007	-
0.7390	19350	0.0021	-
0.7409	19400	0.0001	-
0.7428	19450	0.0001	-
0.7447	19500	0.0001	-
0.7466	19550	0.0001	-
0.7485	19600	0.0001	-
0.7504	19650	0.0001	-
0.7523	19700	0.0001	-
0.7542	19750	0.0001	-
0.7562	19800	0.0001	-
0.7581	19850	0.0001	-
0.7600	19900	0.0001	-
0.7619	19950	0.0001	-
0.7638	20000	0.0001	-
0.7657	20050	0.0001	-
0.7676	20100	0.0001	-
0.7695	20150	0.0001	-
0.7714	20200	0.0001	-
0.7733	20250	0.0001	-
0.7753	20300	0.0001	-
0.7772	20350	0.0001	-
0.7791	20400	0.0001	-
0.7810	20450	0.0001	-
0.7829	20500	0.0001	-
0.7848	20550	0.0001	-
0.7867	20600	0.0072	-
0.7886	20650	0.0034	-
0.7905	20700	0.0001	-
0.7924	20750	0.0002	-
0.7943	20800	0.0001	-
0.7963	20850	0.0001	-
0.7982	20900	0.0004	-
0.8001	20950	0.0001	-
0.8020	21000	0.0001	-
0.8039	21050	0.0001	-
0.8058	21100	0.0001	-
0.8077	21150	0.0001	-
0.8096	21200	0.0001	-
0.8115	21250	0.0001	-
0.8134	21300	0.0001	-
0.8154	21350	0.0001	-
0.8173	21400	0.0001	-
0.8192	21450	0.0001	-
0.8211	21500	0.0002	-
0.8230	21550	0.0002	-
0.8249	21600	0.0001	-
0.8268	21650	0.0001	-
0.8287	21700	0.0001	-
0.8306	21750	0.0001	-
0.8325	21800	0.0001	-
0.8344	21850	0.0019	-
0.8364	21900	0.0001	-
0.8383	21950	0.0001	-
0.8402	22000	0.0001	-
0.8421	22050	0.0001	-
0.8440	22100	0.0001	-
0.8459	22150	0.0001	-
0.8478	22200	0.0001	-
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3.9316	102950	0.0	-
3.9335	103000	0.0	-
3.9355	103050	0.0	-
3.9374	103100	0.0	-
3.9393	103150	0.0	-
3.9412	103200	0.0	-
3.9431	103250	0.0	-
3.9450	103300	0.0	-
3.9469	103350	0.0	-
3.9488	103400	0.0	-
3.9507	103450	0.0	-
3.9526	103500	0.0	-
3.9546	103550	0.0	-
3.9565	103600	0.0	-
3.9584	103650	0.0	-
3.9603	103700	0.0	-
3.9622	103750	0.0	-
3.9641	103800	0.0	-
3.9660	103850	0.0	-
3.9679	103900	0.0	-
3.9698	103950	0.0	-
3.9717	104000	0.0	-
3.9736	104050	0.0	-
3.9756	104100	0.0	-
3.9775	104150	0.0	-
3.9794	104200	0.0	-
3.9813	104250	0.0	-
3.9832	104300	0.0	-
3.9851	104350	0.0	-
3.9870	104400	0.0	-
3.9889	104450	0.0	-
3.9908	104500	0.0	-
3.9927	104550	0.0	-
3.9947	104600	0.0	-
3.9966	104650	0.0	-
3.9985	104700	0.0	-
4.0	104740	-	0.3749

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
SetFit: 1.0.3
Sentence Transformers: 2.2.2
Transformers: 4.36.2
PyTorch: 2.1.2+cu121
Datasets: 2.16.1
Tokenizers: 0.15.0

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}
}