metadata

library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
base_model: sentence-transformers/paraphrase-mpnet-base-v2
metrics:
  - accuracy
widget:
  - text: i'm busy with a project can we talk later
  - text: are you situated in india by any chance
  - text: '35'
  - text: at the tone please record your message
  - text: i told you not to keep going
pipeline_tag: text-classification
inference: true

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: 29 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
decline	'no i am not registered with medicare for part a or part b' "no i'm not in" "thank you very much but i don't think i will need that"
provide_age	'my age is 36' "i'm 62 years old" '49'
complain_calls	'stop disrupting my life with these calls' 'your constant calls are causing me distress' 'same as i was last time you called'
already	'i already charged it should be good to go' 'already took care of it no worries' 'got the app installed already'
Not_Interested	"oh but i'm not interested" "no i don't want to talk i don't want to talk" "i'm not interested in making any decisions right now"
DNC	'i asked you to stop calling me' "i've had enough no more calls" 'would you take me off your list please'
where_get_number	"i never provided my number to you what's going on" 'who is responsible for sharing my number with you' "what's the source of my contact details in your database"
language_barrier	"speak espanol i'm lost" "sorry i'm not speaking english" 'no english'
answering_machine	'this is the voicemail system record your message after the beep' 'this is the voicemail speak your message after the beep' 'you have reached a law source number that is no longer in service please check the number you have dialed'
BUSY	"i'm engaged in a conference call can we talk later" "right now i'm not able to talk right now bye" "i don't have time"
where_are_you_calling_from	'is the philippines where your organization is based' 'where can i find your headquarters' "can you confirm if you're in canada"
scam	"that's none of your business" "i don't think i'm going to say it" 'scammers are clever what measures do you have to counteract them'
affirmation	'yeah you are' "for sure that's correct" 'i have secured both medicare part a and part b coverage'
transfer_request	'i want to discuss this with someone higher up' 'transfer my call to your superior please' 'i require assistance from your manager immediately'
abusive	'what the fuck you calling me for' 'and you rather the fucking guy you fucked up' 'crikey'
calling_about	'why are you getting in touch with me' "what's the main subject of discussion in this call" "what's the rationale behind this call"
GreetBack	"what's crackin' how you been" "i'm doing good how are you doing" "hi i'm fine how's your day been so far"
say_again	'can you please say that again' 'sorry i need you to repeat that' 'what was that can you repeat it'
sorry_greeting	"to be honest i'm feeling a bit down" "i'm not really in a great mood" "i'm not feeling very joyful right now"
not_decision_maker	'decisions like this require a different approach' "decisions of this nature aren't mine to make" 'decisions in this regard are not mine to make'
hold_a_sec	'please stay on the line while i check' 'i need to consult with my colleague hold on' "i'll be right back don't disconnect"
interested	'your topic has piqued my curiosity do continue' 'go' "i'm all ears talk"
greetings	"i'm doing great thank you" "hi there hope you're having a splendid day" 'rest well and recharge good night'
who_are_you	'start by telling me who you are' 'can you tell me your given name' "what's your name and position"
can_you_email	'can you send an email with the instructions' 'can you email me the contract terms' 'is email the preferred way to receive updates'
DNQ	"i'm not the right fit for this" "i'm not the right person for this" "it's not the right fit for me"
are_you_bot	'is this interaction with an automated system' 'is this interaction with a human or a bot' 'is this interaction with a robot or human'
other	'i love exploring the different neighborhoods of our city each has its own charm' 'no i have to buy a car' 'i was just reading an article about the latest technological innovations'
weather	"how's the climate today" 'tell me what the weather is like' "how's the weather in the morning"

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("m-aliabbas1/medicare_idrak_ab")
# Run inference
preds = model("35")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	1	7.3794	109

Label	Training Sample Count
BUSY	528
DNC	585
DNQ	96
GreetBack	224
Not_Interested	497
abusive	145
affirmation	306
already	70
answering_machine	316
are_you_bot	205
calling_about	147
can_you_email	116
complain_calls	65
decline	455
greetings	82
hold_a_sec	79
interested	94
language_barrier	163
not_decision_maker	83
other	56
provide_age	355
say_again	83
scam	110
sorry_greeting	102
transfer_request	73
weather	129
where_are_you_calling_from	250
where_get_number	127
who_are_you	221

Training Hyperparameters

batch_size: (16, 16)
num_epochs: (1, 1)
max_steps: -1
sampling_strategy: oversampling
num_iterations: 40
body_learning_rate: (2e-05, 2e-05)
head_learning_rate: 2e-05
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	0.2366	-
0.0017	50	0.1785	-
0.0035	100	0.1604	-
0.0052	150	0.1877	-
0.0069	200	0.1209	-
0.0087	250	0.161	-
0.0104	300	0.1261	-
0.0121	350	0.153	-
0.0139	400	0.1333	-
0.0156	450	0.0675	-
0.0174	500	0.0623	-
0.0191	550	0.1324	-
0.0208	600	0.0481	-
0.0226	650	0.0894	-
0.0243	700	0.0484	-
0.0260	750	0.0683	-
0.0278	800	0.1025	-
0.0295	850	0.028	-
0.0312	900	0.0218	-
0.0330	950	0.0078	-
0.0347	1000	0.0682	-
0.0364	1050	0.0094	-
0.0382	1100	0.0836	-
0.0399	1150	0.0858	-
0.0417	1200	0.0115	-
0.0434	1250	0.0738	-
0.0451	1300	0.009	-
0.0469	1350	0.044	-
0.0486	1400	0.059	-
0.0503	1450	0.0271	-
0.0521	1500	0.1249	-
0.0538	1550	0.0032	-
0.0555	1600	0.0897	-
0.0573	1650	0.0758	-
0.0590	1700	0.0573	-
0.0607	1750	0.0063	-
0.0625	1800	0.011	-
0.0642	1850	0.005	-
0.0659	1900	0.0545	-
0.0677	1950	0.0216	-
0.0694	2000	0.0059	-
0.0712	2050	0.0043	-
0.0729	2100	0.0109	-
0.0746	2150	0.0049	-
0.0764	2200	0.012	-
0.0781	2250	0.0012	-
0.0798	2300	0.0284	-
0.0816	2350	0.0089	-
0.0833	2400	0.0023	-
0.0850	2450	0.0234	-
0.0868	2500	0.0463	-
0.0885	2550	0.0647	-
0.0902	2600	0.0578	-
0.0920	2650	0.0119	-
0.0937	2700	0.0562	-
0.0955	2750	0.0009	-
0.0972	2800	0.0573	-
0.0989	2850	0.0042	-
0.1007	2900	0.0028	-
0.1024	2950	0.0048	-
0.1041	3000	0.1124	-
0.1059	3050	0.0022	-
0.1076	3100	0.0033	-
0.1093	3150	0.0029	-
0.1111	3200	0.0281	-
0.1128	3250	0.0474	-
0.1145	3300	0.0059	-
0.1163	3350	0.0198	-
0.1180	3400	0.128	-
0.1198	3450	0.0092	-
0.1215	3500	0.0023	-
0.1232	3550	0.044	-
0.1250	3600	0.0333	-
0.1267	3650	0.0014	-
0.1284	3700	0.0019	-
0.1302	3750	0.0514	-
0.1319	3800	0.0004	-
0.1336	3850	0.0022	-
0.1354	3900	0.0012	-
0.1371	3950	0.0598	-
0.1388	4000	0.0013	-
0.1406	4050	0.0597	-
0.1423	4100	0.0004	-
0.1440	4150	0.0038	-
0.1458	4200	0.0523	-
0.1475	4250	0.0481	-
0.1493	4300	0.1062	-
0.1510	4350	0.0033	-
0.1527	4400	0.0007	-
0.1545	4450	0.0002	-
0.1562	4500	0.0009	-
0.1579	4550	0.0021	-
0.1597	4600	0.0013	-
0.1614	4650	0.0012	-
0.1631	4700	0.0012	-
0.1649	4750	0.0016	-
0.1666	4800	0.0002	-
0.1683	4850	0.0005	-
0.1701	4900	0.0039	-
0.1718	4950	0.0013	-
0.1736	5000	0.0022	-
0.1753	5050	0.0006	-
0.1770	5100	0.002	-
0.1788	5150	0.0004	-
0.1805	5200	0.0009	-
0.1822	5250	0.0004	-
0.1840	5300	0.0006	-
0.1857	5350	0.0107	-
0.1874	5400	0.0002	-
0.1892	5450	0.0006	-
0.1909	5500	0.0017	-
0.1926	5550	0.0049	-
0.1944	5600	0.0006	-
0.1961	5650	0.0138	-
0.1978	5700	0.011	-
0.1996	5750	0.0042	-
0.2013	5800	0.0017	-
0.2031	5850	0.0011	-
0.2048	5900	0.0103	-
0.2065	5950	0.0008	-
0.2083	6000	0.0615	-
0.2100	6050	0.0539	-
0.2117	6100	0.0016	-
0.2135	6150	0.0005	-
0.2152	6200	0.0004	-
0.2169	6250	0.0296	-
0.2187	6300	0.0003	-
0.2204	6350	0.0023	-
0.2221	6400	0.0306	-
0.2239	6450	0.0496	-
0.2256	6500	0.0433	-
0.2274	6550	0.0005	-
0.2291	6600	0.0109	-
0.2308	6650	0.0354	-
0.2326	6700	0.0007	-
0.2343	6750	0.0003	-
0.2360	6800	0.0006	-
0.2378	6850	0.0002	-
0.2395	6900	0.0014	-
0.2412	6950	0.0005	-
0.2430	7000	0.0002	-
0.2447	7050	0.0394	-
0.2464	7100	0.0006	-
0.2482	7150	0.0005	-
0.2499	7200	0.0002	-
0.2516	7250	0.0017	-
0.2534	7300	0.0004	-
0.2551	7350	0.0018	-
0.2569	7400	0.0184	-
0.2586	7450	0.0003	-
0.2603	7500	0.0515	-
0.2621	7550	0.0003	-
0.2638	7600	0.0013	-
0.2655	7650	0.0609	-
0.2673	7700	0.0017	-
0.2690	7750	0.0003	-
0.2707	7800	0.0011	-
0.2725	7850	0.0016	-
0.2742	7900	0.003	-
0.2759	7950	0.1212	-
0.2777	8000	0.0001	-
0.2794	8050	0.0004	-
0.2812	8100	0.0003	-
0.2829	8150	0.0608	-
0.2846	8200	0.0002	-
0.2864	8250	0.0003	-
0.2881	8300	0.0022	-
0.2898	8350	0.0052	-
0.2916	8400	0.0003	-
0.2933	8450	0.0001	-
0.2950	8500	0.0007	-
0.2968	8550	0.0336	-
0.2985	8600	0.0071	-
0.3002	8650	0.0002	-
0.3020	8700	0.0002	-
0.3037	8750	0.0107	-
0.3054	8800	0.0006	-
0.3072	8850	0.002	-
0.3089	8900	0.001	-
0.3107	8950	0.0002	-
0.3124	9000	0.0002	-
0.3141	9050	0.0021	-
0.3159	9100	0.0545	-
0.3176	9150	0.0007	-
0.3193	9200	0.0152	-
0.3211	9250	0.0003	-
0.3228	9300	0.0005	-
0.3245	9350	0.053	-
0.3263	9400	0.0031	-
0.3280	9450	0.0002	-
0.3297	9500	0.0002	-
0.3315	9550	0.0002	-
0.3332	9600	0.0009	-
0.3350	9650	0.0023	-
0.3367	9700	0.0011	-
0.3384	9750	0.0003	-
0.3402	9800	0.0003	-
0.3419	9850	0.0005	-
0.3436	9900	0.0004	-
0.3454	9950	0.0028	-
0.3471	10000	0.0016	-
0.3488	10050	0.0008	-
0.3506	10100	0.001	-
0.3523	10150	0.0005	-
0.3540	10200	0.0002	-
0.3558	10250	0.0002	-
0.3575	10300	0.0003	-
0.3593	10350	0.0003	-
0.3610	10400	0.0009	-
0.3627	10450	0.0001	-
0.3645	10500	0.0001	-
0.3662	10550	0.0002	-
0.3679	10600	0.0003	-
0.3697	10650	0.0002	-
0.3714	10700	0.0006	-
0.3731	10750	0.0042	-
0.3749	10800	0.0005	-
0.3766	10850	0.0009	-
0.3783	10900	0.0604	-
0.3801	10950	0.0002	-
0.3818	11000	0.0013	-
0.3835	11050	0.0001	-
0.3853	11100	0.0005	-
0.3870	11150	0.0007	-
0.3888	11200	0.0002	-
0.3905	11250	0.0001	-
0.3922	11300	0.0006	-
0.3940	11350	0.0593	-
0.3957	11400	0.0007	-
0.3974	11450	0.0001	-
0.3992	11500	0.0003	-
0.4009	11550	0.0647	-
0.4026	11600	0.0001	-
0.4044	11650	0.0001	-
0.4061	11700	0.0001	-
0.4078	11750	0.0003	-
0.4096	11800	0.0002	-
0.4113	11850	0.0128	-
0.4131	11900	0.0015	-
0.4148	11950	0.0002	-
0.4165	12000	0.0004	-
0.4183	12050	0.0003	-
0.4200	12100	0.0001	-
0.4217	12150	0.0003	-
0.4235	12200	0.0006	-
0.4252	12250	0.0205	-
0.4269	12300	0.0004	-
0.4287	12350	0.0002	-
0.4304	12400	0.0001	-
0.4321	12450	0.0002	-
0.4339	12500	0.0025	-
0.4356	12550	0.0002	-
0.4373	12600	0.0002	-
0.4391	12650	0.0102	-
0.4408	12700	0.0001	-
0.4426	12750	0.0002	-
0.4443	12800	0.0003	-
0.4460	12850	0.0002	-
0.4478	12900	0.0003	-
0.4495	12950	0.0003	-
0.4512	13000	0.0007	-
0.4530	13050	0.0001	-
0.4547	13100	0.0002	-
0.4564	13150	0.0002	-
0.4582	13200	0.0004	-
0.4599	13250	0.0002	-
0.4616	13300	0.0001	-
0.4634	13350	0.0001	-
0.4651	13400	0.0001	-
0.4669	13450	0.0002	-
0.4686	13500	0.0007	-
0.4703	13550	0.0023	-
0.4721	13600	0.0004	-
0.4738	13650	0.0001	-
0.4755	13700	0.0002	-
0.4773	13750	0.0001	-
0.4790	13800	0.0001	-
0.4807	13850	0.0002	-
0.4825	13900	0.0003	-
0.4842	13950	0.027	-
0.4859	14000	0.0002	-
0.4877	14050	0.0001	-
0.4894	14100	0.0002	-
0.4911	14150	0.0003	-
0.4929	14200	0.0001	-
0.4946	14250	0.0001	-
0.4964	14300	0.0002	-
0.4981	14350	0.0001	-
0.4998	14400	0.0002	-
0.5016	14450	0.0004	-
0.5033	14500	0.0001	-
0.5050	14550	0.0085	-
0.5068	14600	0.0008	-
0.5085	14650	0.0001	-
0.5102	14700	0.0001	-
0.5120	14750	0.0001	-
0.5137	14800	0.044	-
0.5154	14850	0.0001	-
0.5172	14900	0.0001	-
0.5189	14950	0.0001	-
0.5207	15000	0.0002	-
0.5224	15050	0.0001	-
0.5241	15100	0.0001	-
0.5259	15150	0.0003	-
0.5276	15200	0.003	-
0.5293	15250	0.0027	-
0.5311	15300	0.0001	-
0.5328	15350	0.0003	-
0.5345	15400	0.0003	-
0.5363	15450	0.0002	-
0.5380	15500	0.0004	-
0.5397	15550	0.0002	-
0.5415	15600	0.0001	-
0.5432	15650	0.0001	-
0.5449	15700	0.0002	-
0.5467	15750	0.0108	-
0.5484	15800	0.0001	-
0.5502	15850	0.0002	-
0.5519	15900	0.0001	-
0.5536	15950	0.0014	-
0.5554	16000	0.0001	-
0.5571	16050	0.0003	-
0.5588	16100	0.0008	-
0.5606	16150	0.0333	-
0.5623	16200	0.0018	-
0.5640	16250	0.0002	-
0.5658	16300	0.0002	-
0.5675	16350	0.0001	-
0.5692	16400	0.0001	-
0.5710	16450	0.0003	-
0.5727	16500	0.0001	-
0.5745	16550	0.0073	-
0.5762	16600	0.0012	-
0.5779	16650	0.0002	-
0.5797	16700	0.0001	-
0.5814	16750	0.0022	-
0.5831	16800	0.0003	-
0.5849	16850	0.0002	-
0.5866	16900	0.0001	-
0.5883	16950	0.0019	-
0.5901	17000	0.0003	-
0.5918	17050	0.0001	-
0.5935	17100	0.0003	-
0.5953	17150	0.0001	-
0.5970	17200	0.0001	-
0.5988	17250	0.0167	-
0.6005	17300	0.0002	-
0.6022	17350	0.0001	-
0.6040	17400	0.0001	-
0.6057	17450	0.0242	-
0.6074	17500	0.0015	-
0.6092	17550	0.0009	-
0.6109	17600	0.0001	-
0.6126	17650	0.0001	-
0.6144	17700	0.0001	-
0.6161	17750	0.0001	-
0.6178	17800	0.0001	-
0.6196	17850	0.0113	-
0.6213	17900	0.0001	-
0.6230	17950	0.0005	-
0.6248	18000	0.0017	-
0.6265	18050	0.0001	-
0.6283	18100	0.0001	-
0.6300	18150	0.0003	-
0.6317	18200	0.0001	-
0.6335	18250	0.0004	-
0.6352	18300	0.0001	-
0.6369	18350	0.0001	-
0.6387	18400	0.0021	-
0.6404	18450	0.0001	-
0.6421	18500	0.0002	-
0.6439	18550	0.0006	-
0.6456	18600	0.0001	-
0.6473	18650	0.0001	-
0.6491	18700	0.0003	-
0.6508	18750	0.0001	-
0.6526	18800	0.0001	-
0.6543	18850	0.0002	-
0.6560	18900	0.001	-
0.6578	18950	0.0002	-
0.6595	19000	0.0047	-
0.6612	19050	0.0001	-
0.6630	19100	0.0001	-
0.6647	19150	0.0002	-
0.6664	19200	0.0001	-
0.6682	19250	0.0001	-
0.6699	19300	0.0064	-
0.6716	19350	0.0001	-
0.6734	19400	0.0001	-
0.6751	19450	0.0001	-
0.6768	19500	0.0001	-
0.6786	19550	0.0001	-
0.6803	19600	0.0001	-
0.6821	19650	0.0001	-
0.6838	19700	0.0001	-
0.6855	19750	0.0002	-
0.6873	19800	0.0001	-
0.6890	19850	0.0001	-
0.6907	19900	0.0001	-
0.6925	19950	0.0001	-
0.6942	20000	0.0002	-
0.6959	20050	0.0015	-
0.6977	20100	0.0002	-
0.6994	20150	0.0001	-
0.7011	20200	0.0001	-
0.7029	20250	0.0001	-
0.7046	20300	0.0011	-
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0.7567	21800	0.0001	-
0.7584	21850	0.0013	-
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0.7775	22400	0.0001	-
0.7792	22450	0.0013	-
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0.8330	24000	0.0014	-
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0.8886	25600	0.0006	-
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0.9025	26000	0.0001	-
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0.9216	26550	0.0001	-
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0.9320	26850	0.0002	-
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0.9563	27550	0.0035	-
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0.9632	27750	0.0001	-
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0.9667	27850	0.0002	-
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0.9701	27950	0.0001	-
0.9719	28000	0.0001	-
0.9736	28050	0.0001	-
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0.9823	28300	0.0001	-
0.9840	28350	0.0001	-
0.9858	28400	0.0001	-
0.9875	28450	0.0001	-
0.9892	28500	0.0001	-
0.9910	28550	0.0001	-
0.9927	28600	0.0001	-
0.9944	28650	0.0025	-
0.9962	28700	0.0001	-
0.9979	28750	0.0001	-
0.9997	28800	0.0	-

Framework Versions

Python: 3.10.12
SetFit: 1.0.3
Sentence Transformers: 3.0.1
Transformers: 4.39.0
PyTorch: 2.3.0+cu121
Datasets: 2.19.2
Tokenizers: 0.15.2

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}
}