metadata

library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
metrics:
  - accuracy
widget:
  - text: >-
      We will also discuss our deep concerns with actions by China, including in
      Xinjiang, Hong Kong, Taiwan, cyber attacks on the United States, economic
      coercion toward our allies.
  - text: >-
      In the field of bilateral trade and investment, we have agreed that much
      can be done to expand the present level of activity.
  - text: >-
      We cannot allow the world's leading sponsor of terrorism to possess the
      planet's most dangerous weapons.
  - text: >-
      Because I do think this is not a function of whatever happened in Syria, I
      think this is a function of the sanctions.
  - text: >-
      One is to fight inflation, which has been hanging over our head and
      putting a burden on the working people of this country for the last 10
      years.
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/all-mpnet-base-v2

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

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/all-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/all-mpnet-base-v2
Classification head: a LogisticRegression instance
Maximum Sequence Length: 384 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
0	'We in the United States believe if we can promote democracy around the world, there will be more peace.' 'We recognise the transformative power of technology, including digital public infrastructure, to support sustainable development in the Indo-Pacific and deliver economic and social benefits.' 'This program strengthens democracy, transparency, and the rule of law in developing nations, and I ask you to fully fund this important initiative.'
1	'I do not ever want to ever fight a war that is unconstitutional and I am the dangerous person.' "And so, we are at a moment where I really think threats to our democracy, threats to our core freedoms are very much on people's minds." 'My views in opposition to the cancellation of the war debt are a matter of detailed record in many public statements and in a recent message to the Congress.'

Label

Examples

'We in the United States believe if we can promote democracy around the world, there will be more peace.'
'We recognise the transformative power of technology, including digital public infrastructure, to support sustainable development in the Indo-Pacific and deliver economic and social benefits.'
'This program strengthens democracy, transparency, and the rule of law in developing nations, and I ask you to fully fund this important initiative.'

'I do not ever want to ever fight a war that is unconstitutional and I am the dangerous person.'
"And so, we are at a moment where I really think threats to our democracy, threats to our core freedoms are very much on people's minds."
'My views in opposition to the cancellation of the war debt are a matter of detailed record in many public statements and in a recent message to the Congress.'

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("setfit_model_id")
# Run inference
preds = model("We cannot allow the world's leading sponsor of terrorism to possess the planet's most dangerous weapons.")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	3	23.4393	46

Label	Training Sample Count
0	486
1	486

Training Hyperparameters

batch_size: (16, 16)
num_epochs: (1, 1)
max_steps: -1
sampling_strategy: oversampling
body_learning_rate: (1.003444469523018e-06, 1.003444469523018e-06)
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: 37
eval_max_steps: -1
load_best_model_at_end: True

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0000	1	0.3295	-
0.0017	50	0.3132	-
0.0034	100	0.274	-
0.0051	150	0.2774	-
0.0068	200	0.2578	-
0.0084	250	0.2536	-
0.0101	300	0.3353	-
0.0118	350	0.253	-
0.0135	400	0.2865	-
0.0152	450	0.2894	-
0.0169	500	0.2554	0.2632
0.0186	550	0.2487	-
0.0203	600	0.2713	-
0.0220	650	0.2841	-
0.0237	700	0.2251	-
0.0253	750	0.2534	-
0.0270	800	0.2489	-
0.0287	850	0.2297	-
0.0304	900	0.2288	-
0.0321	950	0.211	-
0.0338	1000	0.188	0.2073
0.0355	1050	0.1488	-
0.0372	1100	0.2103	-
0.0389	1150	0.1607	-
0.0406	1200	0.0793	-
0.0422	1250	0.0968	-
0.0439	1300	0.0987	-
0.0456	1350	0.0786	-
0.0473	1400	0.0267	-
0.0490	1450	0.0432	-
0.0507	1500	0.0262	0.064
0.0524	1550	0.1269	-
0.0541	1600	0.039	-
0.0558	1650	0.0266	-
0.0575	1700	0.0455	-
0.0591	1750	0.0175	-
0.0608	1800	0.0157	-
0.0625	1850	0.0063	-
0.0642	1900	0.0146	-
0.0659	1950	0.0046	-
0.0676	2000	0.0046	0.0464
0.0693	2050	0.0035	-
0.0710	2100	0.0073	-
0.0727	2150	0.0012	-
0.0744	2200	0.0025	-
0.0760	2250	0.0023	-
0.0777	2300	0.0017	-
0.0794	2350	0.0012	-
0.0811	2400	0.0017	-
0.0828	2450	0.0016	-
0.0845	2500	0.0014	0.0535
0.0862	2550	0.0011	-
0.0879	2600	0.0021	-
0.0896	2650	0.0009	-
0.0913	2700	0.0008	-
0.0929	2750	0.0006	-
0.0946	2800	0.0007	-
0.0963	2850	0.0012	-
0.0980	2900	0.001	-
0.0997	2950	0.0005	-
0.1014	3000	0.0006	0.0575
0.1031	3050	0.0006	-
0.1048	3100	0.0004	-
0.1065	3150	0.0006	-
0.1082	3200	0.0005	-
0.1098	3250	0.0006	-
0.1115	3300	0.0005	-
0.1132	3350	0.0008	-
0.1149	3400	0.0003	-
0.1166	3450	0.0005	-
0.1183	3500	0.0004	0.0642
0.1200	3550	0.0006	-
0.1217	3600	0.0003	-
0.1234	3650	0.0009	-
0.1251	3700	0.0002	-
0.1267	3750	0.0003	-
0.1284	3800	0.0005	-
0.1301	3850	0.0002	-
0.1318	3900	0.0002	-
0.1335	3950	0.0005	-
0.1352	4000	0.0003	0.0697
0.1369	4050	0.0002	-
0.1386	4100	0.0002	-
0.1403	4150	0.0004	-
0.1420	4200	0.0012	-
0.1436	4250	0.0002	-
0.1453	4300	0.0002	-
0.1470	4350	0.0001	-
0.1487	4400	0.0002	-
0.1504	4450	0.0002	-
0.1521	4500	0.0003	0.0718
0.1538	4550	0.0003	-
0.1555	4600	0.0002	-
0.1572	4650	0.0002	-
0.1589	4700	0.0003	-
0.1605	4750	0.0002	-
0.1622	4800	0.0002	-
0.1639	4850	0.0002	-
0.1656	4900	0.0002	-
0.1673	4950	0.0002	-
0.1690	5000	0.0002	0.0684
0.1707	5050	0.0002	-
0.1724	5100	0.0002	-
0.1741	5150	0.0002	-
0.1758	5200	0.0003	-
0.1774	5250	0.0002	-
0.1791	5300	0.0001	-
0.1808	5350	0.0002	-
0.1825	5400	0.0001	-
0.1842	5450	0.0001	-
0.1859	5500	0.0001	0.0731
0.1876	5550	0.0002	-
0.1893	5600	0.0002	-
0.1910	5650	0.0001	-
0.1927	5700	0.0001	-
0.1943	5750	0.0001	-
0.1960	5800	0.0002	-
0.1977	5850	0.0001	-
0.1994	5900	0.0003	-
0.2011	5950	0.0002	-
0.2028	6000	0.0002	0.0724
0.2045	6050	0.0001	-
0.2062	6100	0.0001	-
0.2079	6150	0.0001	-
0.2096	6200	0.0001	-
0.2112	6250	0.0001	-
0.2129	6300	0.0002	-
0.2146	6350	0.0001	-
0.2163	6400	0.0001	-
0.2180	6450	0.0001	-
0.2197	6500	0.0001	0.0784
0.2214	6550	0.0001	-
0.2231	6600	0.0001	-
0.2248	6650	0.0001	-
0.2265	6700	0.0001	-
0.2281	6750	0.0001	-
0.2298	6800	0.0001	-
0.2315	6850	0.0001	-
0.2332	6900	0.0001	-
0.2349	6950	0.0002	-
0.2366	7000	0.0001	0.0672
0.2383	7050	0.0001	-
0.2400	7100	0.0001	-
0.2417	7150	0.0001	-
0.2434	7200	0.0001	-
0.2450	7250	0.0001	-
0.2467	7300	0.0001	-
0.2484	7350	0.0001	-
0.2501	7400	0.0001	-
0.2518	7450	0.0001	-
0.2535	7500	0.0001	0.0627
0.2552	7550	0.0001	-
0.2569	7600	0.0001	-
0.2586	7650	0.0	-
0.2603	7700	0.0001	-
0.2619	7750	0.0	-
0.2636	7800	0.0001	-
0.2653	7850	0.0001	-
0.2670	7900	0.0001	-
0.2687	7950	0.0001	-
0.2704	8000	0.0	0.0754
0.2721	8050	0.0001	-
0.2738	8100	0.0001	-
0.2755	8150	0.0	-
0.2772	8200	0.0	-
0.2788	8250	0.0	-
0.2805	8300	0.0001	-
0.2822	8350	0.0001	-
0.2839	8400	0.0001	-
0.2856	8450	0.0	-
0.2873	8500	0.0	0.0748
0.2890	8550	0.0	-
0.2907	8600	0.0	-
0.2924	8650	0.0	-
0.2941	8700	0.0	-
0.2957	8750	0.0001	-
0.2974	8800	0.0001	-
0.2991	8850	0.0001	-
0.3008	8900	0.0	-
0.3025	8950	0.0001	-
0.3042	9000	0.0001	0.057
0.3059	9050	0.0	-
0.3076	9100	0.0	-
0.3093	9150	0.0002	-
0.3110	9200	0.0	-
0.3126	9250	0.0	-
0.3143	9300	0.0	-
0.3160	9350	0.0001	-
0.3177	9400	0.0002	-
0.3194	9450	0.0	-
0.3211	9500	0.0	0.0781
0.3228	9550	0.0	-
0.3245	9600	0.0	-
0.3262	9650	0.0	-
0.3279	9700	0.0	-
0.3295	9750	0.0	-
0.3312	9800	0.0	-
0.3329	9850	0.0	-
0.3346	9900	0.0001	-
0.3363	9950	0.0	-
0.3380	10000	0.0	0.0698
0.3397	10050	0.0	-
0.3414	10100	0.0	-
0.3431	10150	0.0	-
0.3448	10200	0.0	-
0.3464	10250	0.0022	-
0.3481	10300	0.0	-
0.3498	10350	0.0001	-
0.3515	10400	0.0	-
0.3532	10450	0.0	-
0.3549	10500	0.0	0.0698
0.3566	10550	0.0	-
0.3583	10600	0.0	-
0.3600	10650	0.0	-
0.3617	10700	0.0	-
0.3633	10750	0.0	-
0.3650	10800	0.0	-
0.3667	10850	0.0	-
0.3684	10900	0.0001	-
0.3701	10950	0.0	-
0.3718	11000	0.0	0.0746
0.3735	11050	0.0	-
0.3752	11100	0.0	-
0.3769	11150	0.0001	-
0.3786	11200	0.0	-
0.3802	11250	0.0	-
0.3819	11300	0.0	-
0.3836	11350	0.0	-
0.3853	11400	0.0	-
0.3870	11450	0.0	-
0.3887	11500	0.0	0.0753
0.3904	11550	0.0	-
0.3921	11600	0.0001	-
0.3938	11650	0.0	-
0.3955	11700	0.0	-
0.3971	11750	0.0	-
0.3988	11800	0.0	-
0.4005	11850	0.0	-
0.4022	11900	0.0	-
0.4039	11950	0.0	-
0.4056	12000	0.0	0.0743
0.4073	12050	0.0	-
0.4090	12100	0.0	-
0.4107	12150	0.0	-
0.4124	12200	0.0	-
0.4140	12250	0.0	-
0.4157	12300	0.0	-
0.4174	12350	0.0	-
0.4191	12400	0.0	-
0.4208	12450	0.0	-
0.4225	12500	0.0	0.0733
0.4242	12550	0.0	-
0.4259	12600	0.0	-
0.4276	12650	0.0	-
0.4293	12700	0.0	-
0.4309	12750	0.0	-
0.4326	12800	0.0	-
0.4343	12850	0.0	-
0.4360	12900	0.0	-
0.4377	12950	0.0	-
0.4394	13000	0.0	0.072
0.4411	13050	0.0	-
0.4428	13100	0.0	-
0.4445	13150	0.0	-
0.4462	13200	0.0	-
0.4478	13250	0.0	-
0.4495	13300	0.0	-
0.4512	13350	0.0	-
0.4529	13400	0.0	-
0.4546	13450	0.0	-
0.4563	13500	0.0	0.0753
0.4580	13550	0.0	-
0.4597	13600	0.0	-
0.4614	13650	0.0	-
0.4631	13700	0.0	-
0.4647	13750	0.0	-
0.4664	13800	0.0	-
0.4681	13850	0.0	-
0.4698	13900	0.0	-
0.4715	13950	0.0	-
0.4732	14000	0.0	0.0756
0.4749	14050	0.0	-
0.4766	14100	0.0	-
0.4783	14150	0.0	-
0.4800	14200	0.0	-
0.4816	14250	0.0	-
0.4833	14300	0.0	-
0.4850	14350	0.0	-
0.4867	14400	0.0	-
0.4884	14450	0.0	-
0.4901	14500	0.0	0.0622
0.4918	14550	0.0	-
0.4935	14600	0.0	-
0.4952	14650	0.0	-
0.4969	14700	0.0	-
0.4985	14750	0.0	-
0.5002	14800	0.0	-
0.5019	14850	0.0	-
0.5036	14900	0.0	-
0.5053	14950	0.0	-
0.5070	15000	0.0	0.0676
0.5087	15050	0.0	-
0.5104	15100	0.0	-
0.5121	15150	0.0	-
0.5138	15200	0.0	-
0.5154	15250	0.0	-
0.5171	15300	0.0	-
0.5188	15350	0.0	-
0.5205	15400	0.0	-
0.5222	15450	0.0	-
0.5239	15500	0.0	0.0668
0.5256	15550	0.0	-
0.5273	15600	0.0	-
0.5290	15650	0.0	-
0.5307	15700	0.0	-
0.5323	15750	0.0	-
0.5340	15800	0.0	-
0.5357	15850	0.0	-
0.5374	15900	0.0	-
0.5391	15950	0.0	-
0.5408	16000	0.0	0.0707
0.5425	16050	0.0	-
0.5442	16100	0.0	-
0.5459	16150	0.0	-
0.5476	16200	0.0	-
0.5492	16250	0.0	-
0.5509	16300	0.0	-
0.5526	16350	0.0	-
0.5543	16400	0.0	-
0.5560	16450	0.0	-
0.5577	16500	0.0	0.0644
0.5594	16550	0.0	-
0.5611	16600	0.0	-
0.5628	16650	0.0	-
0.5645	16700	0.0	-
0.5661	16750	0.0	-
0.5678	16800	0.0	-
0.5695	16850	0.0	-
0.5712	16900	0.0	-
0.5729	16950	0.0	-
0.5746	17000	0.0	0.0742
0.5763	17050	0.0	-
0.5780	17100	0.0	-
0.5797	17150	0.0	-
0.5814	17200	0.0	-
0.5830	17250	0.0	-
0.5847	17300	0.0	-
0.5864	17350	0.0	-
0.5881	17400	0.0	-
0.5898	17450	0.0	-
0.5915	17500	0.0	0.0738
0.5932	17550	0.0	-
0.5949	17600	0.0	-
0.5966	17650	0.0	-
0.5983	17700	0.0	-
0.5999	17750	0.0	-
0.6016	17800	0.0	-
0.6033	17850	0.0	-
0.6050	17900	0.0	-
0.6067	17950	0.0	-
0.6084	18000	0.0	0.0725
0.6101	18050	0.0	-
0.6118	18100	0.0	-
0.6135	18150	0.0	-
0.6152	18200	0.0	-
0.6168	18250	0.0	-
0.6185	18300	0.0	-
0.6202	18350	0.0	-
0.6219	18400	0.0	-
0.6236	18450	0.0	-
0.6253	18500	0.0	0.0724
0.6270	18550	0.0	-
0.6287	18600	0.0	-
0.6304	18650	0.0	-
0.6321	18700	0.0	-
0.6337	18750	0.0	-
0.6354	18800	0.0	-
0.6371	18850	0.0	-
0.6388	18900	0.0	-
0.6405	18950	0.0	-
0.6422	19000	0.0	0.0622
0.6439	19050	0.0	-
0.6456	19100	0.0	-
0.6473	19150	0.0	-
0.6490	19200	0.0	-
0.6506	19250	0.0	-
0.6523	19300	0.0	-
0.6540	19350	0.0	-
0.6557	19400	0.0	-
0.6574	19450	0.0	-
0.6591	19500	0.0	0.0754
0.6608	19550	0.0	-
0.6625	19600	0.0	-
0.6642	19650	0.0	-
0.6659	19700	0.0	-
0.6675	19750	0.0	-
0.6692	19800	0.0	-
0.6709	19850	0.0	-
0.6726	19900	0.0	-
0.6743	19950	0.0	-
0.6760	20000	0.0	0.0723
0.6777	20050	0.0	-
0.6794	20100	0.0	-
0.6811	20150	0.0	-
0.6828	20200	0.0	-
0.6844	20250	0.0	-
0.6861	20300	0.0	-
0.6878	20350	0.0	-
0.6895	20400	0.0	-
0.6912	20450	0.0	-
0.6929	20500	0.0	0.0741
0.6946	20550	0.0	-
0.6963	20600	0.0	-
0.6980	20650	0.0	-
0.6997	20700	0.0	-
0.7013	20750	0.0	-
0.7030	20800	0.0	-
0.7047	20850	0.0	-
0.7064	20900	0.0	-
0.7081	20950	0.0	-
0.7098	21000	0.0	0.0733
0.7115	21050	0.0	-
0.7132	21100	0.0	-
0.7149	21150	0.0	-
0.7166	21200	0.0	-
0.7182	21250	0.0	-
0.7199	21300	0.0	-
0.7216	21350	0.0	-
0.7233	21400	0.0	-
0.7250	21450	0.0	-
0.7267	21500	0.0	0.0757
0.7284	21550	0.0	-
0.7301	21600	0.0	-
0.7318	21650	0.0	-
0.7335	21700	0.0	-
0.7351	21750	0.0	-
0.7368	21800	0.0	-
0.7385	21850	0.0	-
0.7402	21900	0.0	-
0.7419	21950	0.0	-
0.7436	22000	0.0	0.0766
0.7453	22050	0.0	-
0.7470	22100	0.0	-
0.7487	22150	0.0	-
0.7504	22200	0.0	-
0.7520	22250	0.0	-
0.7537	22300	0.0	-
0.7554	22350	0.0	-
0.7571	22400	0.0	-
0.7588	22450	0.0	-
0.7605	22500	0.0	0.0757
0.7622	22550	0.0	-
0.7639	22600	0.0	-
0.7656	22650	0.0	-
0.7673	22700	0.0	-
0.7689	22750	0.0	-
0.7706	22800	0.0	-
0.7723	22850	0.0	-
0.7740	22900	0.0	-
0.7757	22950	0.0	-
0.7774	23000	0.0	0.0755
0.7791	23050	0.0	-
0.7808	23100	0.0	-
0.7825	23150	0.0	-
0.7842	23200	0.0	-
0.7858	23250	0.0	-
0.7875	23300	0.0	-
0.7892	23350	0.0	-
0.7909	23400	0.0	-
0.7926	23450	0.0	-
0.7943	23500	0.0	0.076
0.7960	23550	0.0	-
0.7977	23600	0.0	-
0.7994	23650	0.0	-
0.8011	23700	0.0	-
0.8027	23750	0.0	-
0.8044	23800	0.0	-
0.8061	23850	0.0	-
0.8078	23900	0.0	-
0.8095	23950	0.0	-
0.8112	24000	0.0	0.0756
0.8129	24050	0.0	-
0.8146	24100	0.0	-
0.8163	24150	0.0	-
0.8180	24200	0.0	-
0.8196	24250	0.0	-
0.8213	24300	0.0	-
0.8230	24350	0.0	-
0.8247	24400	0.0	-
0.8264	24450	0.0	-
0.8281	24500	0.0	0.0759
0.8298	24550	0.0	-
0.8315	24600	0.0	-
0.8332	24650	0.0	-
0.8349	24700	0.0	-
0.8365	24750	0.0	-
0.8382	24800	0.0	-
0.8399	24850	0.0	-
0.8416	24900	0.0	-
0.8433	24950	0.0	-
0.8450	25000	0.0	0.0762
0.8467	25050	0.0	-
0.8484	25100	0.0	-
0.8501	25150	0.0	-
0.8518	25200	0.0	-
0.8534	25250	0.0	-
0.8551	25300	0.0	-
0.8568	25350	0.0	-
0.8585	25400	0.0	-
0.8602	25450	0.0	-
0.8619	25500	0.0	0.0733
0.8636	25550	0.0	-
0.8653	25600	0.0	-
0.8670	25650	0.0	-
0.8687	25700	0.0	-
0.8703	25750	0.0	-
0.8720	25800	0.0	-
0.8737	25850	0.0	-
0.8754	25900	0.0	-
0.8771	25950	0.0	-
0.8788	26000	0.0	0.0742
0.8805	26050	0.0	-
0.8822	26100	0.0	-
0.8839	26150	0.0	-
0.8856	26200	0.0	-
0.8872	26250	0.0	-
0.8889	26300	0.0	-
0.8906	26350	0.0	-
0.8923	26400	0.0	-
0.8940	26450	0.0	-
0.8957	26500	0.0	0.0756
0.8974	26550	0.0	-
0.8991	26600	0.0	-
0.9008	26650	0.0	-
0.9025	26700	0.0	-
0.9041	26750	0.0	-
0.9058	26800	0.0	-
0.9075	26850	0.0	-
0.9092	26900	0.0	-
0.9109	26950	0.0	-
0.9126	27000	0.0	0.0751
0.9143	27050	0.0	-
0.9160	27100	0.0	-
0.9177	27150	0.0	-
0.9194	27200	0.0	-
0.9210	27250	0.0	-
0.9227	27300	0.0	-
0.9244	27350	0.0	-
0.9261	27400	0.0	-
0.9278	27450	0.0	-
0.9295	27500	0.0	0.075
0.9312	27550	0.0	-
0.9329	27600	0.0	-
0.9346	27650	0.0	-
0.9363	27700	0.0	-
0.9379	27750	0.0	-
0.9396	27800	0.0	-
0.9413	27850	0.0	-
0.9430	27900	0.0	-
0.9447	27950	0.0	-
0.9464	28000	0.0	0.0725
0.9481	28050	0.0	-
0.9498	28100	0.0	-
0.9515	28150	0.0	-
0.9532	28200	0.0	-
0.9548	28250	0.0	-
0.9565	28300	0.0	-
0.9582	28350	0.0	-
0.9599	28400	0.0	-
0.9616	28450	0.0	-
0.9633	28500	0.0	0.0761
0.9650	28550	0.0	-
0.9667	28600	0.0	-
0.9684	28650	0.0	-
0.9701	28700	0.0	-
0.9717	28750	0.0	-
0.9734	28800	0.0	-
0.9751	28850	0.0	-
0.9768	28900	0.0	-
0.9785	28950	0.0	-
0.9802	29000	0.0	0.0759
0.9819	29050	0.0	-
0.9836	29100	0.0	-
0.9853	29150	0.0	-
0.9870	29200	0.0	-
0.9886	29250	0.0	-
0.9903	29300	0.0	-
0.9920	29350	0.0	-
0.9937	29400	0.0	-
0.9954	29450	0.0	-
0.9971	29500	0.0	0.0761
0.9988	29550	0.0	-

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.11
SetFit: 1.0.1
Sentence Transformers: 2.2.2
Transformers: 4.25.1
PyTorch: 2.1.2
Datasets: 2.15.0
Tokenizers: 0.13.3

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}
}