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: 9 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
5	'please show us the evidence I asked for' 'Please follow https://youtu.be/WpTCt-S-qLM ??' 'Answer the question. The first is illegal in NY, the second is legal.'
0	'This guy sounds like he needs to clear his throat' 'If only most senates in the US can see how climate change can not only effect our planets environment, it also our economys like theirs.' '1st Comment!'
7	'Yeah I can do that.' "I can totally accept that the government fund green energy, it's for the best." 'Yes, he is right ! My Dr. did exactly what he is saying. He started antibiotics then 5 days started the steroids. Hopefully other dr will do the same.'
2	'ok boomer' 'I can see where ur coming from she did invite this cousin to live there which mind you is likely an act of kindness, and it is ur private space. But tbh you are also being extremely petty, this man is on the couch, he has early morning shifts, using ur bathroom would not disturb a single person, while using ur roommates means this man has to be very uncomfortable walk through a sleeping persons room every morning and sneak back out again. Likely waking up the cousin in the process. Thats why I believe its an Everyone Sucks here cause theyre both asshole like moves.' 'suggesting the vaccine to women who are pregnant, when it can cause for many women earlier heavier longer periods, means it triggers a period, we know women in our personal lives that have experienced that along with their period coming twice that month, and we know to avoid foods that can trigger a period because that could potentially cause a miscarriage, so why suggest it?? this is how the whole world will lose confidence in science and medicine because they can down right lie to our face claiming they understand more then you meanwhile propagating the agenda of pharmaceutical companies. absaloutly disgusting and shame on you for betraying our trust.'
3	'Nothing is hotter than Shawn, not even the sun mate??' 'No Im not trolling.' "I'm not being hurtful. I'm being honest. You need to vaccinate your cat"
6	'So youre taking a government course?' 'Is this journalist on work experience ?' 'Who is here after the movie'
4	'Forward looking required now, which the leaders are doing and doing their best, sleep deprived and a world of responsibility on them. Thanks to all' "Oh, great, you could do that? That'd help me out really." "Couldn't be more proud and happy that these heroes are finally taking a stand to the horrible ways the current government is pushing the country"
1	"Maybe STOP paying commanders who have NEVER even picked up a hose and give the power back to the brigades CAPTAIN'S. And things will start to get better, from someone who is actually doing something. Snowy mountains Australia." "@Keith Bawden You have an education in science? Me too! Did you study in any field relevant to the topic? I am currently doing a PhD in biogeochemistry, and my research group is involved in climate science. I can tell you the VAST majority of scientists in fields directly related to or peripheral to climate change accept that it is indeed a real phenomenon, and it is caused by humans. The exceptions you can name are exactly that, exceptions. I'll grant you, Zarkhova may be right about a coming grand solar minimum, but even if so, all it would do is slightly slow temperature increase. There would be no mini ice age (Fuelner and Rahmstorf, 2010). The question is, in 2-3 years, when a 'mini ice age' does not occur, will you change your mind, or find some other reason to deny?" "You should have gotten herd immunity in Changi, considering 95% efficacy of Pfizer and 80% or more are vaccinated.\r\nIt's either the efficacy is faked or the vaccine is useless against the indian variant."
8	"Things got out of hand, I'm sorry." 'Oh no, I did not mean it that way, it was completely misunderstood what I was saying. Didnt mean to offend you, sorry!' 'Sorry.'

Evaluation

Metrics

Label	Metric
all	0.4483

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("CrisisNarratives/setfit-9classes-single_label")
# Run inference
preds = model("my dad had huge ones..so they may be real..")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	1	25.8891	1681

Label	Training Sample Count
0	156
1	81
2	64
3	52
4	46
5	63
6	35
7	37
8	7

Training Hyperparameters

batch_size: (16, 16)
num_epochs: (3, 3)
max_steps: -1
sampling_strategy: oversampling
num_iterations: 40
body_learning_rate: (1.752e-05, 1.752e-05)
head_learning_rate: 1.752e-05
loss: CosineSimilarityLoss
distance_metric: cosine_distance
margin: 0.25
end_to_end: False
use_amp: False
warmup_proportion: 0.1
seed: 30
eval_max_steps: -1
load_best_model_at_end: False

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0004	1	0.3913	-
0.0185	50	0.3901	-
0.0370	100	0.219	-
0.0555	150	0.2308	-
0.0739	200	0.2161	-
0.0924	250	0.2	-
0.1109	300	0.2436	-
0.1294	350	0.2219	-
0.1479	400	0.1266	-
0.1664	450	0.1043	-
0.1848	500	0.076	-
0.2033	550	0.1331	-
0.2218	600	0.0858	-
0.2403	650	0.0355	-
0.2588	700	0.0475	-
0.2773	750	0.066	-
0.2957	800	0.0667	-
0.3142	850	0.0082	-
0.3327	900	0.0658	-
0.3512	950	0.0042	-
0.3697	1000	0.095	-
0.3882	1050	0.0598	-
0.4067	1100	0.0037	-
0.4251	1150	0.0155	-
0.4436	1200	0.0028	-
0.4621	1250	0.0025	-
0.4806	1300	0.0542	-
0.4991	1350	0.001	-
0.5176	1400	0.0056	-
0.5360	1450	0.001	-
0.5545	1500	0.0011	-
0.5730	1550	0.0007	-
0.5915	1600	0.0014	-
0.6100	1650	0.0018	-
0.6285	1700	0.0012	-
0.6470	1750	0.0005	-
0.6654	1800	0.0006	-
0.6839	1850	0.0003	-
0.7024	1900	0.0002	-
0.7209	1950	0.0044	-
0.7394	2000	0.003	-
0.7579	2050	0.0005	-
0.7763	2100	0.0006	-
0.7948	2150	0.0005	-
0.8133	2200	0.0002	-
0.8318	2250	0.0003	-
0.8503	2300	0.0003	-
0.8688	2350	0.0006	-
0.8872	2400	0.0002	-
0.9057	2450	0.002	-
0.9242	2500	0.0003	-
0.9427	2550	0.0002	-
0.9612	2600	0.0009	-
0.9797	2650	0.0001	-
0.9982	2700	0.0002	-
1.0166	2750	0.0003	-
1.0351	2800	0.0003	-
1.0536	2850	0.0004	-
1.0721	2900	0.0003	-
1.0906	2950	0.0004	-
1.1091	3000	0.0003	-
1.1275	3050	0.0001	-
1.1460	3100	0.0002	-
1.1645	3150	0.0005	-
1.1830	3200	0.0004	-
1.2015	3250	0.0003	-
1.2200	3300	0.0003	-
1.2384	3350	0.0004	-
1.2569	3400	0.0003	-
1.2754	3450	0.0002	-
1.2939	3500	0.0002	-
1.3124	3550	0.0003	-
1.3309	3600	0.0005	-
1.3494	3650	0.0002	-
1.3678	3700	0.0003	-
1.3863	3750	0.0002	-
1.4048	3800	0.0001	-
1.4233	3850	0.0001	-
1.4418	3900	0.0004	-
1.4603	3950	0.0001	-
1.4787	4000	0.0002	-
1.4972	4050	0.001	-
1.5157	4100	0.0002	-
1.5342	4150	0.0003	-
1.5527	4200	0.0001	-
1.5712	4250	0.0001	-
1.5896	4300	0.0002	-
1.6081	4350	0.0005	-
1.6266	4400	0.0001	-
1.6451	4450	0.0002	-
1.6636	4500	0.0001	-
1.6821	4550	0.0001	-
1.7006	4600	0.0001	-
1.7190	4650	0.0001	-
1.7375	4700	0.0001	-
1.7560	4750	0.0002	-
1.7745	4800	0.0001	-
1.7930	4850	0.0001	-
1.8115	4900	0.0001	-
1.8299	4950	0.0	-
1.8484	5000	0.0001	-
1.8669	5050	0.0001	-
1.8854	5100	0.0001	-
1.9039	5150	0.0001	-
1.9224	5200	0.0001	-
1.9409	5250	0.0001	-
1.9593	5300	0.0001	-
1.9778	5350	0.0	-
1.9963	5400	0.0001	-
2.0148	5450	0.0001	-
2.0333	5500	0.0001	-
2.0518	5550	0.0001	-
2.0702	5600	0.0002	-
2.0887	5650	0.0001	-
2.1072	5700	0.0001	-
2.1257	5750	0.0	-
2.1442	5800	0.0001	-
2.1627	5850	0.0001	-
2.1811	5900	0.0003	-
2.1996	5950	0.0001	-
2.2181	6000	0.0002	-
2.2366	6050	0.0001	-
2.2551	6100	0.0001	-
2.2736	6150	0.0001	-
2.2921	6200	0.0001	-
2.3105	6250	0.0001	-
2.3290	6300	0.0001	-
2.3475	6350	0.0001	-
2.3660	6400	0.0001	-
2.3845	6450	0.0001	-
2.4030	6500	0.0001	-
2.4214	6550	0.0001	-
2.4399	6600	0.0001	-
2.4584	6650	0.0001	-
2.4769	6700	0.0001	-
2.4954	6750	0.0001	-
2.5139	6800	0.0001	-
2.5323	6850	0.0002	-
2.5508	6900	0.0001	-
2.5693	6950	0.0002	-
2.5878	7000	0.0001	-
2.6063	7050	0.0001	-
2.6248	7100	0.0	-
2.6433	7150	0.0	-
2.6617	7200	0.0001	-
2.6802	7250	0.0001	-
2.6987	7300	0.0002	-
2.7172	7350	0.0001	-
2.7357	7400	0.0001	-
2.7542	7450	0.0002	-
2.7726	7500	0.0	-
2.7911	7550	0.0001	-
2.8096	7600	0.0005	-
2.8281	7650	0.0001	-
2.8466	7700	0.0001	-
2.8651	7750	0.0001	-
2.8835	7800	0.0002	-
2.9020	7850	0.0	-
2.9205	7900	0.0001	-
2.9390	7950	0.0	-
2.9575	8000	0.0001	-
2.9760	8050	0.0001	-
2.9945	8100	0.0001	-
0.0002	1	0.0001	-
0.0108	50	0.0003	-
0.0216	100	0.0001	-
0.0323	150	0.0004	-
0.0431	200	0.0002	-
0.0539	250	0.0006	-
0.0647	300	0.0001	-
0.0755	350	0.0002	-
0.0862	400	0.0051	-
0.0970	450	0.1866	-
0.1078	500	0.11	-
0.1186	550	0.1214	-
0.1294	600	0.2073	-
0.1401	650	0.019	-
0.1509	700	0.0762	-
0.1617	750	0.1901	-
0.1725	800	0.1234	-
0.1833	850	0.0601	-
0.1940	900	0.4192	-
0.2048	950	0.0397	-
0.2156	1000	0.111	-
0.2264	1050	0.055	-
0.2372	1100	0.0146	-
0.2480	1150	0.1277	-
0.2587	1200	0.0236	-
0.2695	1250	0.0087	-
0.2803	1300	0.2315	-
0.2911	1350	0.3547	-
0.3019	1400	0.5957	-
0.3126	1450	0.2253	-
0.3234	1500	0.2068	-
0.3342	1550	0.3203	-
0.3450	1600	0.5608	-
0.3558	1650	0.3014	-
0.3665	1700	0.3287	-
0.3773	1750	0.3206	-
0.3881	1800	0.4245	-
0.3989	1850	0.2641	-
0.4097	1900	0.4057	-
0.4204	1950	0.3891	-
0.4312	2000	0.3688	-
0.4420	2050	0.4642	-
0.4528	2100	0.3684	-
0.4636	2150	0.246	-
0.4743	2200	0.177	-
0.4851	2250	0.3416	-
0.4959	2300	0.3931	-
0.5067	2350	0.2617	-
0.5175	2400	0.5679	-
0.5282	2450	0.3879	-
0.5390	2500	0.3916	-
0.5498	2550	0.3657	-
0.5606	2600	0.3382	-
0.5714	2650	0.4621	-
0.5821	2700	0.3235	-
0.5929	2750	0.2986	-
0.6037	2800	0.3001	-
0.6145	2850	0.2309	-
0.6253	2900	0.1802	-
0.6361	2950	0.2648	-
0.6468	3000	0.2875	-
0.6576	3050	0.2888	-
0.6684	3100	0.2563	-
0.6792	3150	0.3129	-
0.6900	3200	0.2229	-
0.7007	3250	0.2462	-
0.7115	3300	0.283	-
0.7223	3350	0.3622	-
0.7331	3400	0.3428	-
0.7439	3450	0.4274	-
0.7546	3500	0.4131	-
0.7654	3550	0.2123	-
0.7762	3600	0.326	-
0.7870	3650	0.2488	-
0.7978	3700	0.4046	-
0.8085	3750	0.2664	-
0.8193	3800	0.2426	-
0.8301	3850	0.3534	-
0.8409	3900	0.2753	-
0.8517	3950	0.3177	-
0.8624	4000	0.222	-
0.8732	4050	0.3942	-
0.8840	4100	0.1932	-
0.8948	4150	0.2727	-
0.9056	4200	0.2713	-
0.9163	4250	0.3888	-
0.9271	4300	0.3155	-
0.9379	4350	0.2727	-
0.9487	4400	0.4148	-
0.9595	4450	0.297	-
0.9702	4500	0.2154	-
0.9810	4550	0.2617	-
0.9918	4600	0.255	-
1.0026	4650	0.395	-
1.0134	4700	0.4104	-
1.0241	4750	0.2675	-
1.0349	4800	0.2458	-
1.0457	4850	0.316	-
1.0565	4900	0.3786	-
1.0673	4950	0.2206	-
1.0781	5000	0.3946	-
1.0888	5050	0.2178	-
1.0996	5100	0.302	-
1.1104	5150	0.2449	-
1.1212	5200	0.2644	-
1.1320	5250	0.3111	-
1.1427	5300	0.3953	-
1.1535	5350	0.2064	-
1.1643	5400	0.3149	-
1.1751	5450	0.2073	-
1.1859	5500	0.3759	-
1.1966	5550	0.2044	-
1.2074	5600	0.2034	-
1.2182	5650	0.2325	-
1.2290	5700	0.2393	-
1.2398	5750	0.3568	-
1.2505	5800	0.2234	-
1.2613	5850	0.2428	-
1.2721	5900	0.3561	-
1.2829	5950	0.1885	-
1.2937	6000	0.3153	-
1.3044	6050	0.2288	-
1.3152	6100	0.2852	-
1.3260	6150	0.289	-
1.3368	6200	0.3719	-
1.3476	6250	0.1921	-
1.3583	6300	0.266	-
1.3691	6350	0.2743	-
1.3799	6400	0.3637	-
1.3907	6450	0.3849	-
1.4015	6500	0.1926	-
1.4122	6550	0.3594	-
1.4230	6600	0.3263	-
1.4338	6650	0.4645	-
1.4446	6700	0.2304	-
1.4554	6750	0.2337	-
1.4661	6800	0.2812	-
1.4769	6850	0.2975	-
1.4877	6900	0.4025	-
1.4985	6950	0.1897	-
1.5093	7000	0.4523	-
1.5201	7050	0.1906	-
1.5308	7100	0.2756	-
1.5416	7150	0.3313	-
1.5524	7200	0.2999	-
1.5632	7250	0.2517	-
1.5740	7300	0.2421	-
1.5847	7350	0.2864	-
1.5955	7400	0.3119	-
1.6063	7450	0.2178	-
1.6171	7500	0.4006	-
1.6279	7550	0.2744	-
1.6386	7600	0.2306	-
1.6494	7650	0.2772	-
1.6602	7700	0.2103	-
1.6710	7750	0.3151	-
1.6818	7800	0.3457	-
1.6925	7850	0.2146	-
1.7033	7900	0.2105	-
1.7141	7950	0.1986	-
1.7249	8000	0.2604	-
1.7357	8050	0.1683	-
1.7464	8100	0.2814	-
1.7572	8150	0.2088	-
1.7680	8200	0.3935	-
1.7788	8250	0.3019	-
1.7896	8300	0.3094	-
1.8003	8350	0.2024	-
1.8111	8400	0.2901	-
1.8219	8450	0.2392	-
1.8327	8500	0.3296	-
1.8435	8550	0.2818	-
1.8542	8600	0.2898	-
1.8650	8650	0.2598	-
1.8758	8700	0.3531	-
1.8866	8750	0.2989	-
1.8974	8800	0.2356	-
1.9082	8850	0.3657	-
1.9189	8900	0.3765	-
1.9297	8950	0.2668	-
1.9405	9000	0.4219	-
1.9513	9050	0.3362	-
1.9621	9100	0.325	-
1.9728	9150	0.267	-
1.9836	9200	0.2945	-
1.9944	9250	0.2129	-
2.0052	9300	0.351	-
2.0160	9350	0.4508	-
2.0267	9400	0.2375	-
2.0375	9450	0.2669	-
2.0483	9500	0.232	-
2.0591	9550	0.2469	-
2.0699	9600	0.2644	-
2.0806	9650	0.239	-
2.0914	9700	0.3189	-
2.1022	9750	0.2711	-
2.1130	9800	0.2627	-
2.1238	9850	0.2213	-
2.1345	9900	0.2311	-
2.1453	9950	0.3009	-
2.1561	10000	0.2068	-
2.1669	10050	0.3129	-
2.1777	10100	0.2901	-
2.1884	10150	0.2743	-
2.1992	10200	0.2809	-
2.2100	10250	0.249	-
2.2208	10300	0.3017	-
2.2316	10350	0.4271	-
2.2423	10400	0.2551	-
2.2531	10450	0.1911	-
2.2639	10500	0.3297	-
2.2747	10550	0.3251	-
2.2855	10600	0.267	-
2.2962	10650	0.3022	-
2.3070	10700	0.2353	-
2.3178	10750	0.3533	-
2.3286	10800	0.216	-
2.3394	10850	0.3003	-
2.3502	10900	0.2943	-
2.3609	10950	0.2959	-
2.3717	11000	0.3203	-
2.3825	11050	0.2962	-
2.3933	11100	0.2475	-
2.4041	11150	0.2933	-
2.4148	11200	0.2903	-
2.4256	11250	0.328	-
2.4364	11300	0.1893	-
2.4472	11350	0.2367	-
2.4580	11400	0.2473	-
2.4687	11450	0.2751	-
2.4795	11500	0.2708	-
2.4903	11550	0.3104	-
2.5011	11600	0.2791	-
2.5119	11650	0.3181	-
2.5226	11700	0.2411	-
2.5334	11750	0.3114	-
2.5442	11800	0.2759	-
2.5550	11850	0.3006	-
2.5658	11900	0.2647	-
2.5765	11950	0.225	-
2.5873	12000	0.2904	-
2.5981	12050	0.2776	-
2.6089	12100	0.3102	-
2.6197	12150	0.2499	-
2.6304	12200	0.2763	-
2.6412	12250	0.2645	-
2.6520	12300	0.3281	-
2.6628	12350	0.1793	-
2.6736	12400	0.3369	-
2.6843	12450	0.2598	-
2.6951	12500	0.3334	-
2.7059	12550	0.2935	-
2.7167	12600	0.4243	-
2.7275	12650	0.2212	-
2.7382	12700	0.2187	-
2.7490	12750	0.3004	-
2.7598	12800	0.4244	-
2.7706	12850	0.2242	-
2.7814	12900	0.3072	-
2.7922	12950	0.3468	-
2.8029	13000	0.2112	-
2.8137	13050	0.2935	-
2.8245	13100	0.2618	-
2.8353	13150	0.266	-
2.8461	13200	0.2458	-
2.8568	13250	0.2244	-
2.8676	13300	0.2764	-
2.8784	13350	0.2262	-
2.8892	13400	0.2232	-
2.9000	13450	0.2353	-
2.9107	13500	0.3661	-
2.9215	13550	0.1905	-
2.9323	13600	0.3493	-
2.9431	13650	0.2481	-
2.9539	13700	0.23	-
2.9646	13750	0.2407	-
2.9754	13800	0.2673	-
2.9862	13850	0.2091	-
2.9970	13900	0.2471	-
0.0004	1	0.287	-
0.0185	50	0.285	-
0.0370	100	0.233	-
0.0555	150	0.2874	-
0.0739	200	0.2599	-
0.0924	250	0.284	-
0.1109	300	0.3046	-
0.1294	350	0.2751	-
0.1479	400	0.2343	-
0.1664	450	0.2809	-
0.1848	500	0.2178	-
0.2033	550	0.2654	-
0.2218	600	0.2673	-
0.2403	650	0.2628	-
0.2588	700	0.279	-
0.2773	750	0.2448	-
0.2957	800	0.2502	-
0.3142	850	0.3343	-
0.3327	900	0.2669	-
0.3512	950	0.2714	-
0.3697	1000	0.3234	-
0.3882	1050	0.2892	-
0.4067	1100	0.2437	-
0.4251	1150	0.2409	-
0.4436	1200	0.2402	-
0.4621	1250	0.2479	-
0.4806	1300	0.2323	-
0.4991	1350	0.2474	-
0.5176	1400	0.319	-
0.5360	1450	0.3341	-
0.5545	1500	0.3162	-
0.5730	1550	0.2973	-
0.5915	1600	0.2975	-
0.6100	1650	0.2828	-
0.6285	1700	0.2625	-
0.6470	1750	0.2769	-
0.6654	1800	0.271	-
0.6839	1850	0.2538	-
0.7024	1900	0.1979	-
0.7209	1950	0.3117	-
0.7394	2000	0.2247	-
0.7579	2050	0.3248	-
0.7763	2100	0.2515	-
0.7948	2150	0.2877	-
0.8133	2200	0.3182	-
0.8318	2250	0.2772	-
0.8503	2300	0.2423	-
0.8688	2350	0.2638	-
0.8872	2400	0.226	-
0.9057	2450	0.306	-
0.9242	2500	0.2072	-
0.9427	2550	0.2434	-
0.9612	2600	0.2712	-
0.9797	2650	0.3225	-
0.9982	2700	0.2534	-
1.0166	2750	0.2364	-
1.0351	2800	0.241	-
1.0536	2850	0.2165	-
1.0721	2900	0.2719	-
1.0906	2950	0.2694	-
1.1091	3000	0.2562	-
1.1275	3050	0.2994	-
1.1460	3100	0.2477	-
1.1645	3150	0.231	-
1.1830	3200	0.2751	-
1.2015	3250	0.2543	-
1.2200	3300	0.2468	-
1.2384	3350	0.217	-
1.2569	3400	0.2664	-
1.2754	3450	0.2556	-
1.2939	3500	0.2334	-
1.3124	3550	0.2396	-
1.3309	3600	0.2383	-
1.3494	3650	0.2635	-
1.3678	3700	0.2652	-
1.3863	3750	0.2573	-
1.4048	3800	0.2211	-
1.4233	3850	0.2244	-
1.4418	3900	0.2399	-
1.4603	3950	0.2587	-
1.4787	4000	0.304	-
1.4972	4050	0.287	-
1.5157	4100	0.2667	-
1.5342	4150	0.3251	-
1.5527	4200	0.2641	-
1.5712	4250	0.2576	-
1.5896	4300	0.3057	-
1.6081	4350	0.2145	-
1.6266	4400	0.2665	-
1.6451	4450	0.2756	-
1.6636	4500	0.3089	-
1.6821	4550	0.3013	-
1.7006	4600	0.2337	-
1.7190	4650	0.2538	-
1.7375	4700	0.2428	-
1.7560	4750	0.2694	-
1.7745	4800	0.2367	-
1.7930	4850	0.2656	-
1.8115	4900	0.2405	-
1.8299	4950	0.2381	-
1.8484	5000	0.2363	-
1.8669	5050	0.2395	-
1.8854	5100	0.3183	-
1.9039	5150	0.2918	-
1.9224	5200	0.2985	-
1.9409	5250	0.3331	-
1.9593	5300	0.2716	-
1.9778	5350	0.2529	-
1.9963	5400	0.2557	-
2.0148	5450	0.2618	-
2.0333	5500	0.296	-
2.0518	5550	0.2866	-
2.0702	5600	0.2445	-
2.0887	5650	0.2464	-
2.1072	5700	0.2247	-
2.1257	5750	0.2906	-
2.1442	5800	0.2413	-
2.1627	5850	0.2805	-
2.1811	5900	0.2777	-
2.1996	5950	0.2151	-
2.2181	6000	0.2938	-
2.2366	6050	0.2569	-
2.2551	6100	0.2523	-
2.2736	6150	0.2649	-
2.2921	6200	0.2265	-
2.3105	6250	0.216	-
2.3290	6300	0.3309	-
2.3475	6350	0.2815	-
2.3660	6400	0.2566	-
2.3845	6450	0.237	-
2.4030	6500	0.2165	-
2.4214	6550	0.2975	-
2.4399	6600	0.2402	-
2.4584	6650	0.2943	-
2.4769	6700	0.2522	-
2.4954	6750	0.2473	-
2.5139	6800	0.2652	-
2.5323	6850	0.244	-
2.5508	6900	0.2488	-
2.5693	6950	0.2726	-
2.5878	7000	0.2282	-
2.6063	7050	0.2386	-
2.6248	7100	0.3269	-
2.6433	7150	0.2401	-
2.6617	7200	0.284	-
2.6802	7250	0.3263	-
2.6987	7300	0.3019	-
2.7172	7350	0.2364	-
2.7357	7400	0.2219	-
2.7542	7450	0.2798	-
2.7726	7500	0.2605	-
2.7911	7550	0.2958	-
2.8096	7600	0.2028	-
2.8281	7650	0.2577	-
2.8466	7700	0.2686	-
2.8651	7750	0.2894	-
2.8835	7800	0.3136	-
2.9020	7850	0.2417	-
2.9205	7900	0.276	-
2.9390	7950	0.2608	-
2.9575	8000	0.2545	-
2.9760	8050	0.2539	-
2.9945	8100	0.1995	-

Framework Versions

Python: 3.9.16
SetFit: 1.0.1
Sentence Transformers: 2.2.2
Transformers: 4.35.0
PyTorch: 2.1.0+cu121
Datasets: 2.14.6
Tokenizers: 0.14.1

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}
}

CrisisNarratives
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setfit-9classes-single_label