This is a SetFit model that can be used for Text Classification. This SetFit model uses microsoft/harrier-oss-v1-0.6b 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: microsoft/harrier-oss-v1-0.6b
Classification head: a LogisticRegression instance
Maximum Sequence Length: 32768 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
positive	'The 1980s Latin American debt crisis revealed the dangers of predatory lending, a problem that persists today in the form of sovereign debt traps.' 'The widespread burnout among health workers is now a workforce crisis.' 'The melting of permafrost in the Arctic is releasing vast amounts of methane, accelerating the pace of global warming.'
negative	'Crisis: The Battle for Europe is a wargame where players must navigate the complex alliances and betrayals of World War I.' 'With enhanced disaster preparedness exercises, emergency responders are improving coordination across agencies.' 'The department is conducting a review of its environmental policies.'

Evaluation

Metrics

Label	F1_Macro	F1_Binary
all	0.9814	0.9795

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

python

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("fefofico/crisis_trained")
# Run inference
preds = model("The startup’s leadership is under intense strain after the product failed to gain users.")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	5	16.7702	37

Label	Training Sample Count
negative	973
positive	768

Training Hyperparameters

batch_size: (16, 16)
num_epochs: (10, 10)
max_steps: -1
sampling_strategy: oversampling
num_iterations: 20
body_learning_rate: (2e-05, 2e-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
l2_weight: 0.01
seed: 42
eval_max_steps: -1
load_best_model_at_end: False

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0002	1	0.2579	-
0.0046	20	0.244	-
0.0092	40	0.2415	-
0.0138	60	0.2461	-
0.0184	80	0.2425	-
0.0230	100	0.2317	-
0.0276	120	0.2398	-
0.0322	140	0.2295	-
0.0368	160	0.2279	-
0.0414	180	0.214	-
0.0459	200	0.1889	-
0.0505	220	0.1617	-
0.0551	240	0.0965	-
0.0597	260	0.0372	-
0.0643	280	0.0179	-
0.0689	300	0.0047	-
0.0735	320	0.0046	-
0.0781	340	0.0043	-
0.0827	360	0.0038	-
0.0873	380	0.0018	-
0.0919	400	0.0006	-
0.0965	420	0.0013	-
0.1011	440	0.0004	-
0.1057	460	0.0004	-
0.1103	480	0.0003	-
0.1149	500	0.0003	-
0.1195	520	0.0002	-
0.1241	540	0.0002	-
0.1286	560	0.0002	-
0.1332	580	0.0002	-
0.1378	600	0.0002	-
0.1424	620	0.0002	-
0.1470	640	0.0001	-
0.1516	660	0.0002	-
0.1562	680	0.0001	-
0.1608	700	0.0001	-
0.1654	720	0.0001	-
0.1700	740	0.0001	-
0.1746	760	0.0001	-
0.1792	780	0.0001	-
0.1838	800	0.0001	-
0.1884	820	0.0001	-
0.1930	840	0.0001	-
0.1976	860	0.0001	-
0.2022	880	0.0001	-
0.2068	900	0.0001	-
0.2113	920	0.0001	-
0.2159	940	0.0	-
0.2205	960	0.0	-
0.2251	980	0.0	-
0.2297	1000	0.0	-
0.2343	1020	0.0	-
0.2389	1040	0.0	-
0.2435	1060	0.0	-
0.2481	1080	0.0	-
0.2527	1100	0.0	-
0.2573	1120	0.0	-
0.2619	1140	0.0	-
0.2665	1160	0.0	-
0.2711	1180	0.0	-
0.2757	1200	0.0	-
0.2803	1220	0.0	-
0.2849	1240	0.0	-
0.2895	1260	0.0	-
0.2941	1280	0.0	-
0.2986	1300	0.0	-
0.3032	1320	0.0	-
0.3078	1340	0.0	-
0.3124	1360	0.0	-
0.3170	1380	0.0	-
0.3216	1400	0.0	-
0.3262	1420	0.0	-
0.3308	1440	0.0	-
0.3354	1460	0.0	-
0.3400	1480	0.0	-
0.3446	1500	0.0	-
0.3492	1520	0.0	-
0.3538	1540	0.0	-
0.3584	1560	0.0	-
0.3630	1580	0.0	-
0.3676	1600	0.0	-
0.3722	1620	0.0	-
0.3768	1640	0.0	-
0.3813	1660	0.0	-
0.3859	1680	0.0	-
0.3905	1700	0.0	-
0.3951	1720	0.0	-
0.3997	1740	0.0	-
0.4043	1760	0.0	-
0.4089	1780	0.0	-
0.4135	1800	0.0	-
0.4181	1820	0.0	-
0.4227	1840	0.0	-
0.4273	1860	0.0	-
0.4319	1880	0.0	-
0.4365	1900	0.0	-
0.4411	1920	0.0	-
0.4457	1940	0.0	-
0.4503	1960	0.0	-
0.4549	1980	0.0	-
0.4595	2000	0.0	-
0.4640	2020	0.0	-
0.4686	2040	0.0	-
0.4732	2060	0.0	-
0.4778	2080	0.0	-
0.4824	2100	0.0	-
0.4870	2120	0.0	-
0.4916	2140	0.0	-
0.4962	2160	0.0	-
0.5008	2180	0.0	-
0.5054	2200	0.0	-
0.5100	2220	0.0	-
0.5146	2240	0.0	-
0.5192	2260	0.0	-
0.5238	2280	0.0	-
0.5284	2300	0.0	-
0.5330	2320	0.0	-
0.5376	2340	0.0	-
0.5422	2360	0.0	-
0.5467	2380	0.0	-
0.5513	2400	0.0	-
0.5559	2420	0.0	-
0.5605	2440	0.0	-
0.5651	2460	0.0	-
0.5697	2480	0.0	-
0.5743	2500	0.0	-
0.5789	2520	0.0	-
0.5835	2540	0.0	-
0.5881	2560	0.0	-
0.5927	2580	0.0	-
0.5973	2600	0.0	-
0.6019	2620	0.0	-
0.6065	2640	0.0	-
0.6111	2660	0.0	-
0.6157	2680	0.0	-
0.6203	2700	0.0	-
0.6249	2720	0.0	-
0.6295	2740	0.0	-
0.6340	2760	0.0	-
0.6386	2780	0.0	-
0.6432	2800	0.0	-
0.6478	2820	0.0	-
0.6524	2840	0.0	-
0.6570	2860	0.0	-
0.6616	2880	0.0	-
0.6662	2900	0.0	-
0.6708	2920	0.0	-
0.6754	2940	0.0	-
0.6800	2960	0.0	-
0.6846	2980	0.0	-
0.6892	3000	0.0	-
0.6938	3020	0.0	-
0.6984	3040	0.0	-
0.7030	3060	0.0	-
0.7076	3080	0.0	-
0.7122	3100	0.0	-
0.7167	3120	0.0	-
0.7213	3140	0.0	-
0.7259	3160	0.0	-
0.7305	3180	0.0	-
0.7351	3200	0.0	-
0.7397	3220	0.0	-
0.7443	3240	0.0	-
0.7489	3260	0.0	-
0.7535	3280	0.0	-
0.7581	3300	0.0	-
0.7627	3320	0.0	-
0.7673	3340	0.0	-
0.7719	3360	0.0	-
0.7765	3380	0.0	-
0.7811	3400	0.0	-
0.7857	3420	0.0	-
0.7903	3440	0.0	-
0.7949	3460	0.0	-
0.7994	3480	0.0	-
0.8040	3500	0.0	-
0.8086	3520	0.0	-
0.8132	3540	0.0	-
0.8178	3560	0.0	-
0.8224	3580	0.0	-
0.8270	3600	0.0	-
0.8316	3620	0.0	-
0.8362	3640	0.0	-
0.8408	3660	0.0	-
0.8454	3680	0.0	-
0.8500	3700	0.0	-
0.8546	3720	0.0	-
0.8592	3740	0.0	-
0.8638	3760	0.0	-
0.8684	3780	0.0	-
0.8730	3800	0.0	-
0.8776	3820	0.0	-
0.8822	3840	0.0	-
0.8867	3860	0.0	-
0.8913	3880	0.0	-
0.8959	3900	0.0	-
0.9005	3920	0.0	-
0.9051	3940	0.0	-
0.9097	3960	0.0	-
0.9143	3980	0.0	-
0.9189	4000	0.0	-
0.9235	4020	0.0	-
0.9281	4040	0.0	-
0.9327	4060	0.0	-
0.9373	4080	0.0	-
0.9419	4100	0.0	-
0.9465	4120	0.0	-
0.9511	4140	0.0	-
0.9557	4160	0.0	-
0.9603	4180	0.0	-
0.9649	4200	0.0	-
0.9694	4220	0.0	-
0.9740	4240	0.0	-
0.9786	4260	0.0	-
0.9832	4280	0.0	-
0.9878	4300	0.0	-
0.9924	4320	0.0	-
0.9970	4340	0.0	-
1.0	4353	-	0.0050
1.0016	4360	0.0	-
1.0062	4380	0.0	-
1.0108	4400	0.0	-
1.0154	4420	0.0	-
1.0200	4440	0.0	-
1.0246	4460	0.0	-
1.0292	4480	0.0	-
1.0338	4500	0.0	-
1.0384	4520	0.0	-
1.0430	4540	0.0	-
1.0476	4560	0.0	-
1.0521	4580	0.0	-
1.0567	4600	0.0	-
1.0613	4620	0.0	-
1.0659	4640	0.0	-
1.0705	4660	0.0	-
1.0751	4680	0.0	-
1.0797	4700	0.0	-
1.0843	4720	0.0	-
1.0889	4740	0.0	-
1.0935	4760	0.0	-
1.0981	4780	0.0	-
1.1027	4800	0.0	-
1.1073	4820	0.0	-
1.1119	4840	0.0	-
1.1165	4860	0.0	-
1.1211	4880	0.0	-
1.1257	4900	0.0	-
1.1303	4920	0.0	-
1.1348	4940	0.0	-
1.1394	4960	0.0	-
1.1440	4980	0.0	-
1.1486	5000	0.0	-
1.1532	5020	0.0	-
1.1578	5040	0.0	-
1.1624	5060	0.0	-
1.1670	5080	0.0	-
1.1716	5100	0.0	-
1.1762	5120	0.0	-
1.1808	5140	0.0	-
1.1854	5160	0.0	-
1.1900	5180	0.0	-
1.1946	5200	0.0	-
1.1992	5220	0.0	-
1.2038	5240	0.0	-
1.2084	5260	0.0	-
1.2130	5280	0.0	-
1.2176	5300	0.0	-
1.2221	5320	0.0	-
1.2267	5340	0.0	-
1.2313	5360	0.0	-
1.2359	5380	0.0	-
1.2405	5400	0.0	-
1.2451	5420	0.0	-
1.2497	5440	0.0	-
1.2543	5460	0.0	-
1.2589	5480	0.0	-
1.2635	5500	0.0	-
1.2681	5520	0.0	-
1.2727	5540	0.0	-
1.2773	5560	0.0	-
1.2819	5580	0.0	-
1.2865	5600	0.0	-
1.2911	5620	0.0	-
1.2957	5640	0.0	-
1.3003	5660	0.0	-
1.3048	5680	0.0	-
1.3094	5700	0.0	-
1.3140	5720	0.0	-
1.3186	5740	0.0	-
1.3232	5760	0.0	-
1.3278	5780	0.0	-
1.3324	5800	0.0	-
1.3370	5820	0.0	-
1.3416	5840	0.0	-
1.3462	5860	0.0	-
1.3508	5880	0.0	-
1.3554	5900	0.0	-
1.3600	5920	0.0	-
1.3646	5940	0.0	-
1.3692	5960	0.0	-
1.3738	5980	0.0	-
1.3784	6000	0.0	-
1.3830	6020	0.0	-
1.3875	6040	0.0	-
1.3921	6060	0.0	-
1.3967	6080	0.0	-
1.4013	6100	0.0	-
1.4059	6120	0.0	-
1.4105	6140	0.0	-
1.4151	6160	0.0	-
1.4197	6180	0.0	-
1.4243	6200	0.0	-
1.4289	6220	0.0	-
1.4335	6240	0.0	-
1.4381	6260	0.0	-
1.4427	6280	0.0	-
1.4473	6300	0.0	-
1.4519	6320	0.0	-
1.4565	6340	0.0	-
1.4611	6360	0.0	-
1.4657	6380	0.0	-
1.4703	6400	0.0	-
1.4748	6420	0.0	-
1.4794	6440	0.0	-
1.4840	6460	0.0	-
1.4886	6480	0.0	-
1.4932	6500	0.0	-
1.4978	6520	0.0	-
1.5024	6540	0.0	-
1.5070	6560	0.0	-
1.5116	6580	0.0	-
1.5162	6600	0.0	-
1.5208	6620	0.0	-
1.5254	6640	0.0	-
1.5300	6660	0.0	-
1.5346	6680	0.0	-
1.5392	6700	0.0	-
1.5438	6720	0.0	-
1.5484	6740	0.0	-
1.5530	6760	0.0	-
1.5575	6780	0.0	-
1.5621	6800	0.0	-
1.5667	6820	0.0	-
1.5713	6840	0.0	-
1.5759	6860	0.0	-
1.5805	6880	0.0	-
1.5851	6900	0.0	-
1.5897	6920	0.0	-
1.5943	6940	0.0	-
1.5989	6960	0.0	-
1.6035	6980	0.0	-
1.6081	7000	0.0	-
1.6127	7020	0.0	-
1.6173	7040	0.0	-
1.6219	7060	0.0	-
1.6265	7080	0.0	-
1.6311	7100	0.0	-
1.6357	7120	0.0	-
1.6402	7140	0.0	-
1.6448	7160	0.0	-
1.6494	7180	0.0	-
1.6540	7200	0.0	-
1.6586	7220	0.0	-
1.6632	7240	0.0	-
1.6678	7260	0.0	-
1.6724	7280	0.0	-
1.6770	7300	0.0	-
1.6816	7320	0.0	-
1.6862	7340	0.0	-
1.6908	7360	0.0	-
1.6954	7380	0.0	-
1.7000	7400	0.0	-
1.7046	7420	0.0	-
1.7092	7440	0.0	-
1.7138	7460	0.0	-
1.7184	7480	0.0	-
1.7229	7500	0.0	-
1.7275	7520	0.0	-
1.7321	7540	0.0	-
1.7367	7560	0.0	-
1.7413	7580	0.0	-
1.7459	7600	0.0	-
1.7505	7620	0.0	-
1.7551	7640	0.0	-
1.7597	7660	0.0	-
1.7643	7680	0.0	-
1.7689	7700	0.0	-
1.7735	7720	0.0001	-
1.7781	7740	0.0001	-
1.7827	7760	0.0	-
1.7873	7780	0.0	-
1.7919	7800	0.0	-
1.7965	7820	0.0	-
1.8011	7840	0.0	-
1.8057	7860	0.0	-
1.8102	7880	0.0	-
1.8148	7900	0.0	-
1.8194	7920	0.0	-
1.8240	7940	0.0	-
1.8286	7960	0.0	-
1.8332	7980	0.0	-
1.8378	8000	0.0	-
1.8424	8020	0.0	-
1.8470	8040	0.0	-
1.8516	8060	0.0	-
1.8562	8080	0.0	-
1.8608	8100	0.0	-
1.8654	8120	0.0	-
1.8700	8140	0.0	-
1.8746	8160	0.0	-
1.8792	8180	0.0	-
1.8838	8200	0.0	-
1.8884	8220	0.0	-
1.8929	8240	0.0	-
1.8975	8260	0.0	-
1.9021	8280	0.0	-
1.9067	8300	0.0	-
1.9113	8320	0.0	-
1.9159	8340	0.0	-
1.9205	8360	0.0	-
1.9251	8380	0.0	-
1.9297	8400	0.0	-
1.9343	8420	0.0	-
1.9389	8440	0.0	-
1.9435	8460	0.0	-
1.9481	8480	0.0	-
1.9527	8500	0.0	-
1.9573	8520	0.0	-
1.9619	8540	0.0	-
1.9665	8560	0.0	-
1.9711	8580	0.0	-
1.9756	8600	0.0	-
1.9802	8620	0.0	-
1.9848	8640	0.0	-
1.9894	8660	0.0	-
1.9940	8680	0.0	-
1.9986	8700	0.0	-
2.0	8706	-	0.0051
2.0032	8720	0.0	-
2.0078	8740	0.0	-
2.0124	8760	0.0	-
2.0170	8780	0.0	-
2.0216	8800	0.0	-
2.0262	8820	0.0	-
2.0308	8840	0.0	-
2.0354	8860	0.0	-
2.0400	8880	0.0	-
2.0446	8900	0.0	-
2.0492	8920	0.0	-
2.0538	8940	0.0	-
2.0584	8960	0.0	-
2.0629	8980	0.0	-
2.0675	9000	0.0	-
2.0721	9020	0.0	-
2.0767	9040	0.0	-
2.0813	9060	0.0	-
2.0859	9080	0.0	-
2.0905	9100	0.0	-
2.0951	9120	0.0	-
2.0997	9140	0.0	-
2.1043	9160	0.0	-
2.1089	9180	0.0	-
2.1135	9200	0.0	-
2.1181	9220	0.0	-
2.1227	9240	0.0	-
2.1273	9260	0.0	-
2.1319	9280	0.0	-
2.1365	9300	0.0	-
2.1411	9320	0.0	-
2.1456	9340	0.0	-
2.1502	9360	0.0	-
2.1548	9380	0.0	-
2.1594	9400	0.0	-
2.1640	9420	0.0	-
2.1686	9440	0.0	-
2.1732	9460	0.0	-
2.1778	9480	0.0	-
2.1824	9500	0.0	-
2.1870	9520	0.0	-
2.1916	9540	0.0	-
2.1962	9560	0.0	-
2.2008	9580	0.0	-
2.2054	9600	0.0	-
2.2100	9620	0.0	-
2.2146	9640	0.0	-
2.2192	9660	0.0	-
2.2238	9680	0.0	-
2.2283	9700	0.0	-
2.2329	9720	0.0	-
2.2375	9740	0.0	-
2.2421	9760	0.0	-
2.2467	9780	0.0	-
2.2513	9800	0.0	-
2.2559	9820	0.0	-
2.2605	9840	0.0	-
2.2651	9860	0.0	-
2.2697	9880	0.0	-
2.2743	9900	0.0	-
2.2789	9920	0.0	-
2.2835	9940	0.0	-
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9.7955	42640	0.0	-
9.8001	42660	0.0	-
9.8047	42680	0.0	-
9.8093	42700	0.0	-
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9.9931	43500	0.0	-
9.9977	43520	0.0	-
10.0	43530	-	0.0350

Framework Versions

Python: 3.12.13
SetFit: 1.1.3
Sentence Transformers: 3.4.1
Transformers: 4.57.6
PyTorch: 2.10.0+cu128
Datasets: 4.8.5
Tokenizers: 0.22.2

Citation

BibTeX

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}
}