verdict-classifier-en

saattrupdan/verdict-classifier-en

verdict-classifier-en is a text classification model from saattrupdan. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.

This model is a fine-tuned version of roberta-base on 2,500 deduplicated verdicts from Google Fact Check Tools API, translated into English with the Google Cloud Translation API.

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Model card

From the Hugging Face model README

English Verdict Classifier

This model is a fine-tuned version of roberta-base on 2,500 deduplicated verdicts from Google Fact Check Tools API, translated into English with the Google Cloud Translation API. It achieves the following results on the evaluation set, being 1,000 such verdicts translated into English, but here including duplicates to represent the true distribution:

Loss: 0.1290
F1 Macro: 0.9171
F1 Misinformation: 0.9896
F1 Factual: 0.9890
F1 Other: 0.7727
Precision Macro: 0.8940
Precision Misinformation: 0.9954
Precision Factual: 0.9783
Precision Other: 0.7083

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 2500
num_epochs: 1000

Training results

Training Loss	Epoch	Step	Validation Loss	F1 Macro	F1 Misinformation	F1 Factual	F1 Other	Precision Macro	Precision Misinformation	Precision Factual	Precision Other
1.1493	0.16	50	1.1040	0.0550	0.0	0.1650	0.0	0.0300	0.0	0.0899	0.0
1.0899	0.32	100	1.0765	0.0619	0.0203	0.1654	0.0	0.2301	0.6	0.0903	0.0
1.0136	0.48	150	1.0487	0.3102	0.9306	0.0	0.0	0.2900	0.8701	0.0	0.0
0.9868	0.64	200	1.0221	0.3102	0.9306	0.0	0.0	0.2900	0.8701	0.0	0.0
0.9599	0.8	250	0.9801	0.3102	0.9306	0.0	0.0	0.2900	0.8701	0.0	0.0
0.9554	0.96	300	0.9500	0.3102	0.9306	0.0	0.0	0.2900	0.8701	0.0	0.0
0.935	1.12	350	0.9071	0.3102	0.9306	0.0	0.0	0.2900	0.8701	0.0	0.0
0.948	1.28	400	0.8809	0.3102	0.9306	0.0	0.0	0.2900	0.8701	0.0	0.0
0.9344	1.44	450	0.8258	0.3102	0.9306	0.0	0.0	0.2900	0.8701	0.0	0.0
0.9182	1.6	500	0.7687	0.3102	0.9306	0.0	0.0	0.2900	0.8701	0.0	0.0
0.8942	1.76	550	0.5787	0.3102	0.9306	0.0	0.0	0.2900	0.8701	0.0	0.0
0.8932	1.92	600	0.4506	0.4043	0.9628	0.0	0.25	0.3777	0.9753	0.0	0.1579
0.7448	2.08	650	0.2884	0.5323	0.9650	0.3303	0.3017	0.7075	0.9810	0.9474	0.1942
0.6616	2.24	700	0.2162	0.8161	0.9710	0.9724	0.5051	0.7910	0.9824	0.9670	0.4237
0.575	2.4	750	0.1754	0.8305	0.9714	0.9780	0.5421	0.7961	0.9881	0.9674	0.4328
0.5246	2.56	800	0.1641	0.8102	0.9659	0.9175	0.5472	0.7614	0.9892	0.8558	0.4394
0.481	2.72	850	0.1399	0.8407	0.9756	0.9780	0.5686	0.8082	0.9894	0.9674	0.4677
0.4588	2.88	900	0.1212	0.8501	0.9786	0.9783	0.5934	0.8247	0.9871	0.9574	0.5294
0.4512	3.04	950	0.1388	0.8270	0.9702	0.9836	0.5273	0.7904	0.9893	0.9677	0.4143
0.3894	3.2	1000	0.1270	0.8411	0.9737	0.9836	0.5660	0.8043	0.9905	0.9677	0.4545
0.3772	3.36	1050	0.1267	0.8336	0.9732	0.9890	0.5385	0.8013	0.9882	0.9783	0.4375
0.3528	3.52	1100	0.1073	0.8546	0.9791	0.9890	0.5957	0.8284	0.9883	0.9783	0.5185
0.3694	3.68	1150	0.1120	0.8431	0.9786	0.9890	0.5618	0.8244	0.9849	0.9783	0.5102
0.3146	3.84	1200	0.1189	0.8325	0.9738	0.9836	0.54	0.8016	0.9870	0.9677	0.45
0.3038	4.01	1250	0.1041	0.8648	0.9815	0.9836	0.6292	0.8425	0.9884	0.9677	0.5714
0.2482	4.17	1300	0.1245	0.8588	0.9773	0.9836	0.6154	0.8202	0.9929	0.9677	0.5
0.2388	4.33	1350	0.1167	0.8701	0.9808	0.9836	0.6458	0.8377	0.9918	0.9677	0.5536
0.2593	4.49	1400	0.1215	0.8654	0.9790	0.9836	0.6337	0.8284	0.9929	0.9677	0.5246
0.239	4.65	1450	0.1057	0.8621	0.9803	0.9890	0.6170	0.8349	0.9895	0.9783	0.5370
0.2397	4.81	1500	0.1256	0.8544	0.9761	0.9890	0.5981	0.8162	0.9929	0.9783	0.4776
0.2238	4.97	1550	0.1189	0.8701	0.9802	0.9836	0.6465	0.8343	0.9929	0.9677	0.5424
0.1811	5.13	1600	0.1456	0.8438	0.9737	0.9836	0.5741	0.8051	0.9917	0.9677	0.4559
0.1615	5.29	1650	0.1076	0.8780	0.9838	0.9836	0.6667	0.8581	0.9895	0.9677	0.6170
0.1783	5.45	1700	0.1217	0.8869	0.9831	0.9836	0.6939	0.8497	0.9953	0.9677	0.5862
0.1615	5.61	1750	0.1305	0.8770	0.9808	0.9836	0.6667	0.8371	0.9953	0.9677	0.5484
0.155	5.77	1800	0.1218	0.8668	0.9821	0.9890	0.6292	0.8460	0.9884	0.9783	0.5714
0.167	5.93	1850	0.1091	0.8991	0.9873	0.9890	0.7209	0.8814	0.9919	0.9783	0.6739
0.1455	6.09	1900	0.1338	0.8535	0.9773	0.9890	0.5941	0.8202	0.9906	0.9783	0.4918
0.1301	6.25	1950	0.1321	0.8792	0.9820	0.9890	0.6667	0.8439	0.9941	0.9783	0.5593
0.1049	6.41	2000	0.1181	0.9031	0.9879	0.9834	0.7381	0.8911	0.9908	0.9780	0.7045
0.1403	6.57	2050	0.1432	0.8608	0.9779	0.9890	0.6154	0.8237	0.9929	0.9783	0.5
0.1178	6.73	2100	0.1443	0.8937	0.9844	0.9945	0.7021	0.8644	0.9930	0.9890	0.6111
0.1267	6.89	2150	0.1346	0.8494	0.9786	0.9890	0.5806	0.8249	0.9871	0.9783	0.5094
0.1043	7.05	2200	0.1494	0.8905	0.9832	0.9945	0.6939	0.8564	0.9941	0.9890	0.5862
0.0886	7.21	2250	0.1180	0.8946	0.9873	0.9890	0.7073	0.8861	0.9896	0.9783	0.6905
0.1183	7.37	2300	0.1777	0.8720	0.9790	0.9890	0.6481	0.8298	0.9964	0.9783	0.5147
0.0813	7.53	2350	0.1405	0.8912	0.9856	0.9836	0.7045	0.8685	0.9919	0.9677	0.6458
0.111	7.69	2400	0.1379	0.8874	0.9838	0.9836	0.6947	0.8540	0.9941	0.9677	0.6
0.1199	7.85	2450	0.1301	0.9080	0.9879	0.9890	0.7473	0.8801	0.9953	0.9783	0.6667
0.1054	8.01	2500	0.1478	0.8845	0.9838	0.9890	0.6809	0.8546	0.9930	0.9783	0.5926
0.105	8.17	2550	0.1333	0.9021	0.9879	0.9890	0.7294	0.8863	0.9919	0.9783	0.6889
0.09	8.33	2600	0.1555	0.8926	0.9855	0.9890	0.7033	0.8662	0.9930	0.9783	0.6275
0.0947	8.49	2650	0.1572	0.8831	0.9856	0.9890	0.6747	0.8726	0.9885	0.9783	0.6512
0.0784	8.65	2700	0.1477	0.8969	0.9873	0.9890	0.7143	0.8836	0.9908	0.9783	0.6818
0.0814	8.81	2750	0.1700	0.8932	0.9861	0.9890	0.7045	0.8720	0.9919	0.9783	0.6458
0.0962	8.97	2800	0.1290	0.9171	0.9896	0.9890	0.7727	0.8940	0.9954	0.9783	0.7083
0.0802	9.13	2850	0.1721	0.8796	0.9832	0.9890	0.6667	0.8517	0.9918	0.9783	0.5849
0.0844	9.29	2900	0.1516	0.9023	0.9867	0.9890	0.7312	0.8717	0.9953	0.9783	0.6415
0.0511	9.45	2950	0.1544	0.9062	0.9879	0.9890	0.7416	0.8820	0.9942	0.9783	0.6735
0.0751	9.61	3000	0.1748	0.8884	0.9832	0.9945	0.6875	0.8571	0.9930	0.9890	0.5893
0.0707	9.77	3050	0.1743	0.8721	0.9802	0.9890	0.6471	0.8349	0.9941	0.9783	0.5323
0.0951	9.93	3100	0.1660	0.8899	0.9850	0.9890	0.6957	0.8622	0.9930	0.9783	0.6154
0.0576	10.1	3150	0.2029	0.8613	0.9766	0.9890	0.6182	0.8197	0.9952	0.9783	0.4857
0.0727	10.26	3200	0.1709	0.8920	0.9849	0.9890	0.7021	0.8612	0.9942	0.9783	0.6111
0.0654	10.42	3250	0.1599	0.8999	0.9861	0.9945	0.7191	0.8780	0.9919	0.9890	0.6531
0.0553	10.58	3300	0.2091	0.8920	0.9849	0.9890	0.7021	0.8612	0.9942	0.9783	0.6111

Framework versions

Transformers 4.11.3
Pytorch 1.9.0+cu102
Datasets 1.9.0
Tokenizers 0.10.2

Task: Text Classification
Library: transformers
Type: roberta
License: mit
Languages: en
Created: Mar 2, 2022
Updated: Apr 3, 2025