powerinfer-seq-cls

HAO-K/powerinfer-seq-cls

powerinfer-seq-cls is a text classification model from HAO-K. Use it when you need a label for a piece of text. It is set up for transformers.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Text Classification
Library: transformers
Model type: qwen2
Access: Public
Created: Feb 13, 2025
Updated: Feb 16, 2025
SHA: 2c90b2b0

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Base models

Model card

From the Hugging Face model README

powerinfer-seq-cls

This model is a fine-tuned version of PowerInfer/SmallThinker-3B-Preview on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.0290

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 16
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
gradient_accumulation_steps: 16
total_train_batch_size: 256
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine_with_restarts
lr_scheduler_warmup_ratio: 0.1
num_epochs: 20
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
1.7722	0.0571	30	1.4541
0.9933	0.1143	60	0.7808
0.4922	0.1714	90	0.5142
0.3496	0.2286	120	0.3577
0.2746	0.2857	150	0.3452
0.2295	0.3429	180	0.1985
0.1529	0.4000	210	0.1752
0.1471	0.4572	240	0.1584
0.1819	0.5143	270	0.2583
0.1638	0.5715	300	0.2517
0.1294	0.6286	330	0.1405
0.1388	0.6858	360	0.1234
0.1207	0.7429	390	0.1893
0.1079	0.8001	420	0.1293
0.1075	0.8572	450	0.2412
0.1034	0.9144	480	0.2327
0.1196	0.9715	510	0.1451
0.0994	1.0295	540	0.0909
0.1103	1.0867	570	0.0970
0.1109	1.1438	600	0.1160
0.1	1.2010	630	0.1187
0.0858	1.2581	660	0.1644
0.0992	1.3153	690	0.1040
0.0689	1.3724	720	0.0764
0.0681	1.4296	750	0.0853
0.0905	1.4867	780	0.1184
0.0957	1.5439	810	0.1317
0.0868	1.6010	840	0.2817
0.1281	1.6582	870	0.2166
0.0785	1.7153	900	0.0928
0.0738	1.7725	930	0.2114
0.1337	1.8296	960	0.1321
0.1074	1.8868	990	0.1436
0.0829	1.9439	1020	0.1643
0.0942	2.0019	1050	0.1076
0.098	2.0591	1080	0.0937
0.0771	2.1162	1110	0.0598
0.0684	2.1734	1140	0.0551
0.1826	2.2305	1170	0.1241
0.1139	2.2877	1200	0.0567
0.0837	2.3448	1230	0.1561
0.1254	2.4020	1260	0.0968
0.0854	2.4591	1290	0.0820
0.0769	2.5163	1320	0.1383
0.0776	2.5734	1350	0.1381
0.0553	2.6306	1380	0.0787
0.1121	2.6877	1410	0.1219
0.0585	2.7449	1440	0.0777
0.0595	2.8020	1470	0.0906
0.0523	2.8591	1500	0.1116
0.0501	2.9163	1530	0.0475
0.076	2.9734	1560	0.1763
0.0663	3.0314	1590	0.0593
0.0576	3.0886	1620	0.0571
0.0369	3.1457	1650	0.0646
0.0537	3.2029	1680	0.0503
0.0474	3.2600	1710	0.0802
0.0698	3.3172	1740	0.1044
0.0566	3.3743	1770	0.1519
0.0466	3.4315	1800	0.0743
0.045	3.4886	1830	0.0652
0.0565	3.5458	1860	0.0635
0.0325	3.6029	1890	0.0801
0.0415	3.6601	1920	0.0729
0.048	3.7172	1950	0.0544
0.0369	3.7744	1980	0.0577
0.0406	3.8315	2010	0.0514
0.0437	3.8887	2040	0.0552
0.0445	3.9458	2070	0.0773
0.0472	4.0038	2100	0.0496
0.0495	4.0610	2130	0.0641
0.0472	4.1181	2160	0.0457
0.0367	4.1753	2190	0.0638
0.0404	4.2324	2220	0.0868
0.0529	4.2896	2250	0.0360
0.0306	4.3467	2280	0.0610
0.0405	4.4039	2310	0.0797
0.0447	4.4610	2340	0.0750
0.0552	4.5182	2370	0.0374
0.0337	4.5753	2400	0.0397
0.0306	4.6325	2430	0.0565
0.0382	4.6896	2460	0.0534
0.036	4.7468	2490	0.0461
0.0429	4.8039	2520	0.0609
0.0273	4.8611	2550	0.0404
0.0323	4.9182	2580	0.0407
0.0366	4.9754	2610	0.0406
0.0295	5.0333	2640	0.0511
0.0458	5.0905	2670	0.0414
0.0274	5.1476	2700	0.0399
0.0404	5.2048	2730	0.0474
0.0332	5.2619	2760	0.0425
0.0318	5.3191	2790	0.0532
0.0336	5.3762	2820	0.0397
0.03	5.4334	2850	0.0383
0.0289	5.4905	2880	0.0513
0.0355	5.5477	2910	0.0321
0.0283	5.6048	2940	0.0406
0.0269	5.6620	2970	0.0300
0.0239	5.7191	3000	0.0424
0.0285	5.7763	3030	0.0430
0.0283	5.8334	3060	0.0440
0.0328	5.8906	3090	0.0558
0.0273	5.9477	3120	0.0366
0.0516	6.0057	3150	0.0690
0.0468	6.0629	3180	0.0417
0.0323	6.1200	3210	0.0457
0.0259	6.1772	3240	0.0385
0.0302	6.2343	3270	0.0382
0.0254	6.2915	3300	0.0426
0.0312	6.3486	3330	0.0378
0.0289	6.4058	3360	0.0356
0.0388	6.4629	3390	0.0767
0.0294	6.5201	3420	0.0464
0.0303	6.5772	3450	0.0373
0.0247	6.6344	3480	0.0638
0.0213	6.6915	3510	0.0408
0.0298	6.7487	3540	0.0443
0.0229	6.8058	3570	0.0400
0.0194	6.8630	3600	0.0399
0.024	6.9201	3630	0.0435
0.0217	6.9773	3660	0.0387
0.0229	7.0352	3690	0.0383
0.0269	7.0924	3720	0.0404
0.0296	7.1495	3750	0.0724
0.0271	7.2067	3780	0.0490
0.0245	7.2638	3810	0.0377
0.0255	7.3210	3840	0.0407
0.0226	7.3781	3870	0.0369
0.0223	7.4353	3900	0.0341
0.0247	7.4924	3930	0.0385
0.0234	7.5496	3960	0.0369
0.0222	7.6067	3990	0.0372
0.014	7.6639	4020	0.0391
0.0252	7.7210	4050	0.0432
0.0318	7.7782	4080	0.0397
0.0254	7.8353	4110	0.0398
0.0262	7.8925	4140	0.0352
0.0199	7.9496	4170	0.0382
0.024	8.0076	4200	0.0363
0.0241	8.0648	4230	0.0327
0.0238	8.1219	4260	0.0336
0.0212	8.1791	4290	0.0372
0.0207	8.2362	4320	0.0336
0.0265	8.2934	4350	0.0369
0.0211	8.3505	4380	0.0498
0.0242	8.4077	4410	0.0635
0.0257	8.4648	4440	0.0594
0.0205	8.5220	4470	0.0565
0.0215	8.5791	4500	0.0487
0.0211	8.6363	4530	0.0449
0.0257	8.6934	4560	0.0452
0.0248	8.7506	4590	0.0401
0.0187	8.8077	4620	0.0419
0.0175	8.8649	4650	0.0434
0.0333	8.9220	4680	0.0457
0.0238	8.9792	4710	0.0466
0.0232	9.0371	4740	0.0474
0.0231	9.0943	4770	0.0502
0.0232	9.1514	4800	0.0479
0.0177	9.2086	4830	0.0476
0.0296	9.2657	4860	0.0497
0.023	9.3229	4890	0.0457
0.0237	9.3800	4920	0.0465
0.0221	9.4372	4950	0.0461
0.0231	9.4943	4980	0.0309
0.0221	9.5515	5010	0.0329
0.0198	9.6086	5040	0.0334
0.0194	9.6658	5070	0.0308
0.0188	9.7229	5100	0.0281
0.0168	9.7801	5130	0.0276
0.0199	9.8372	5160	0.0282
0.0202	9.8944	5190	0.0288
0.0148	9.9515	5220	0.0301
0.0152	10.0095	5250	0.0303
0.015	10.0667	5280	0.0302
0.0145	10.1238	5310	0.0300
0.0181	10.1810	5340	0.0303
0.0174	10.2381	5370	0.0300
0.0166	10.2953	5400	0.0300
0.0208	10.3524	5430	0.0300
0.0201	10.4096	5460	0.0301
0.0159	10.4667	5490	0.0302
0.0192	10.5239	5520	0.0302
0.0114	10.5810	5550	0.0302
0.0198	10.6382	5580	0.0302
0.0186	10.6953	5610	0.0301
0.0178	10.7525	5640	0.0299
0.0174	10.8096	5670	0.0298
0.0179	10.8668	5700	0.0298
0.0169	10.9239	5730	0.0298
0.0159	10.9811	5760	0.0298
0.02	11.0391	5790	0.0298
0.0163	11.0962	5820	0.0298
0.0191	11.1534	5850	0.0273
0.0188	11.2105	5880	0.0412
0.0327	11.2677	5910	0.0970
0.0592	11.3248	5940	0.1420
0.0542	11.3820	5970	0.0973
0.0776	11.4391	6000	0.0648
0.0597	11.4962	6030	0.1299
0.0566	11.5534	6060	0.0834
0.049	11.6105	6090	0.0560
0.0638	11.6677	6120	0.0450
0.0412	11.7248	6150	0.1571
0.0606	11.7820	6180	0.1047
0.0363	11.8391	6210	0.0540
0.0315	11.8963	6240	0.0546
0.0397	11.9534	6270	0.0920
0.0272	12.0114	6300	0.0515
0.0402	12.0686	6330	0.0834
0.0393	12.1257	6360	0.0482
0.0302	12.1829	6390	0.0605
0.0354	12.2400	6420	0.1459
0.0379	12.2972	6450	0.0443
0.0317	12.3543	6480	0.0440
0.0278	12.4115	6510	0.0516
0.0224	12.4686	6540	0.0533
0.031	12.5258	6570	0.0474
0.0469	12.5829	6600	0.1061
0.0739	12.6401	6630	0.0689
0.0301	12.6972	6660	0.0511
0.0344	12.7544	6690	0.0428
0.0305	12.8115	6720	0.0441
0.0282	12.8687	6750	0.0439
0.0238	12.9258	6780	0.0448
0.041	12.9830	6810	0.0395
0.0273	13.0410	6840	0.0427
0.0284	13.0981	6870	0.0736
0.0296	13.1553	6900	0.0824
0.3257	13.2124	6930	0.1177
0.1631	13.2696	6960	0.0749
0.0364	13.3267	6990	0.0722
0.0289	13.3839	7020	0.0501
0.0252	13.4410	7050	0.0722
0.033	13.4982	7080	0.0498
0.045	13.5553	7110	0.0712
0.0354	13.6125	7140	0.0592
0.0255	13.6696	7170	0.0491
0.0382	13.7268	7200	0.1050
0.0373	13.7839	7230	0.0591
0.0381	13.8411	7260	0.0626
0.0265	13.8982	7290	0.0612
0.0263	13.9554	7320	0.0870
0.0299	14.0133	7350	0.0602
0.0289	14.0705	7380	0.0501
0.0255	14.1276	7410	0.0454
0.0223	14.1848	7440	0.0581
0.0246	14.2419	7470	0.0464
0.0221	14.2991	7500	0.0478
0.0286	14.3562	7530	0.0839
0.0279	14.4134	7560	0.0606
0.0287	14.4705	7590	0.0443
0.0186	14.5277	7620	0.0591
0.0213	14.5848	7650	0.0548
0.0239	14.6420	7680	0.0619
0.0258	14.6991	7710	0.0739
0.0385	14.7563	7740	0.0575
0.0338	14.8134	7770	0.0795
0.0321	14.8706	7800	0.0482
0.0271	14.9277	7830	0.0462
0.0236	14.9849	7860	0.0465
0.0249	15.0429	7890	0.0438
0.0214	15.1000	7920	0.0697
0.0295	15.1572	7950	0.0677
0.0257	15.2143	7980	0.0403
0.0247	15.2715	8010	0.0490
0.0272	15.3286	8040	0.0453
0.0197	15.3858	8070	0.0507
0.0209	15.4429	8100	0.0561
0.0155	15.5001	8130	0.0569
0.0243	15.5572	8160	0.0494
0.0247	15.6144	8190	0.0508
0.0305	15.6715	8220	0.0517
0.0219	15.7287	8250	0.0370
0.0239	15.7858	8280	0.0427
0.0231	15.8430	8310	0.0311
0.0215	15.9001	8340	0.0315
0.0202	15.9573	8370	0.0288
0.0258	16.0152	8400	0.0299
0.021	16.0724	8430	0.0289
0.0191	16.1295	8460	0.0296
0.019	16.1867	8490	0.0310
0.0233	16.2438	8520	0.0365
0.0183	16.3010	8550	0.0275
0.0203	16.3581	8580	0.0296
0.0218	16.4153	8610	0.0282
0.0172	16.4724	8640	0.0274
0.0197	16.5296	8670	0.0273
0.0189	16.5867	8700	0.0329
0.0174	16.6439	8730	0.0285
0.019	16.7010	8760	0.0294
0.0191	16.7582	8790	0.0379
0.0246	16.8153	8820	0.0348
0.0179	16.8725	8850	0.0372
0.0207	16.9296	8880	0.0449
0.0195	16.9868	8910	0.0361
0.0148	17.0448	8940	0.0388
0.0193	17.1019	8970	0.0430
0.0122	17.1591	9000	0.0422
0.0167	17.2162	9030	0.0337
0.0218	17.2734	9060	0.0345
0.0173	17.3305	9090	0.0389
0.02	17.3877	9120	0.0464
0.0155	17.4448	9150	0.0391
0.0212	17.5020	9180	0.0370
0.0187	17.5591	9210	0.0362
0.0195	17.6163	9240	0.0367
0.0221	17.6734	9270	0.0443
0.0191	17.7306	9300	0.0375
0.0199	17.7877	9330	0.0391
0.0201	17.8449	9360	0.0373
0.0179	17.9020	9390	0.0356
0.0178	17.9592	9420	0.0375
0.0202	18.0171	9450	0.0323
0.0186	18.0743	9480	0.0364
0.0156	18.1314	9510	0.0299
0.0151	18.1886	9540	0.0295
0.0174	18.2457	9570	0.0295
0.0175	18.3029	9600	0.0309
0.0166	18.3600	9630	0.0315
0.0176	18.4172	9660	0.0305
0.0196	18.4743	9690	0.0308
0.0142	18.5315	9720	0.0328
0.0175	18.5886	9750	0.0311
0.0199	18.6458	9780	0.0304
0.0132	18.7029	9810	0.0305
0.016	18.7601	9840	0.0305
0.0172	18.8172	9870	0.0300
0.0146	18.8744	9900	0.0299
0.0187	18.9315	9930	0.0300
0.016	18.9887	9960	0.0300
0.0151	19.0467	9990	0.0304
0.0146	19.1038	10020	0.0308
0.0151	19.1610	10050	0.0307
0.0156	19.2181	10080	0.0305
0.0181	19.2753	10110	0.0287
0.0147	19.3324	10140	0.0285
0.0139	19.3896	10170	0.0283
0.0145	19.4467	10200	0.0282
0.0195	19.5039	10230	0.0286
0.0146	19.5610	10260	0.0290
0.0139	19.6182	10290	0.0291
0.0199	19.6753	10320	0.0290
0.0131	19.7325	10350	0.0289
0.0149	19.7896	10380	0.0290
0.0176	19.8468	10410	0.0291
0.0172	19.9039	10440	0.0290
0.0167	19.9611	10470	0.0290

Framework versions

Transformers 4.46.2
Pytorch 2.5.1+cu124
Datasets 3.2.0
Tokenizers 0.20.3

Task: Text Classification
Library: transformers
Type: qwen2
Created: Feb 13, 2025
Updated: Feb 16, 2025