clip-roberta-finetuned

This model is a fine-tuned version of ./clip-roberta on the davanstrien/manuscript_noisy_labels_iiif dataset. It achieves the following results on the evaluation set:

• Loss: 2.5792

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: 5e-05
• train_batch_size: 128
• eval_batch_size: 256
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
2.9841	0.07	500	3.4112
2.72	0.15	1000	3.3430
2.6319	0.22	1500	3.2295
2.5781	0.29	2000	3.1645
2.5339	0.36	2500	3.1226
2.503	0.44	3000	3.0856
2.4581	0.51	3500	3.0639
2.4494	0.58	4000	3.0415
2.4275	0.65	4500	3.0245
2.3909	0.73	5000	2.9991
2.3902	0.8	5500	2.9931
2.3741	0.87	6000	2.9612
2.3536	0.95	6500	2.9509
2.3392	1.02	7000	2.9289
2.3083	1.09	7500	2.9214
2.3094	1.16	8000	2.9153
2.2864	1.24	8500	2.9034
2.2893	1.31	9000	2.8963
2.2697	1.38	9500	2.8847
2.2762	1.46	10000	2.8665
2.2667	1.53	10500	2.8536
2.2548	1.6	11000	2.8472
2.238	1.67	11500	2.8491
2.2423	1.75	12000	2.8257
2.2406	1.82	12500	2.8287
2.2248	1.89	13000	2.8193
2.223	1.96	13500	2.8101
2.1995	2.04	14000	2.8027
2.1834	2.11	14500	2.7880
2.1723	2.18	15000	2.7783
2.1651	2.26	15500	2.7739
2.1575	2.33	16000	2.7825
2.1598	2.4	16500	2.7660
2.1667	2.47	17000	2.7578
2.1565	2.55	17500	2.7580
2.1558	2.62	18000	2.7561
2.1642	2.69	18500	2.7512
2.1374	2.77	19000	2.7361
2.1402	2.84	19500	2.7385
2.1326	2.91	20000	2.7235
2.1272	2.98	20500	2.7183
2.0954	3.06	21000	2.7156
2.0842	3.13	21500	2.7065
2.0859	3.2	22000	2.7089
2.0856	3.27	22500	2.6962
2.0775	3.35	23000	2.6931
2.0821	3.42	23500	2.6933
2.0706	3.49	24000	2.7011
2.0689	3.57	24500	2.7009
2.0807	3.64	25000	2.6825
2.0639	3.71	25500	2.6744
2.0742	3.78	26000	2.6777
2.0789	3.86	26500	2.6689
2.0594	3.93	27000	2.6566
2.056	4.0	27500	2.6676
2.0223	4.08	28000	2.6711
2.0185	4.15	28500	2.6568
2.018	4.22	29000	2.6567
2.0036	4.29	29500	2.6545
2.0238	4.37	30000	2.6559
2.0091	4.44	30500	2.6450
2.0096	4.51	31000	2.6389
2.0083	4.58	31500	2.6401
2.0012	4.66	32000	2.6399
2.0166	4.73	32500	2.6289
1.9963	4.8	33000	2.6348
1.9943	4.88	33500	2.6240
2.0099	4.95	34000	2.6190
1.9895	5.02	34500	2.6308
1.9581	5.09	35000	2.6385
1.9502	5.17	35500	2.6237
1.9485	5.24	36000	2.6248
1.9643	5.31	36500	2.6279
1.9535	5.38	37000	2.6185
1.9575	5.46	37500	2.6146
1.9475	5.53	38000	2.6093
1.9434	5.6	38500	2.6090
1.954	5.68	39000	2.6027
1.9509	5.75	39500	2.6107
1.9454	5.82	40000	2.5980
1.9479	5.89	40500	2.6016
1.9539	5.97	41000	2.5971
1.9119	6.04	41500	2.6228
1.8974	6.11	42000	2.6169
1.9038	6.19	42500	2.6027
1.9008	6.26	43000	2.6027
1.9142	6.33	43500	2.6011
1.8783	6.4	44000	2.5960
1.8896	6.48	44500	2.6111
1.8975	6.55	45000	2.5889
1.9048	6.62	45500	2.6007
1.9049	6.69	46000	2.5972
1.8969	6.77	46500	2.6053
1.9105	6.84	47000	2.5893
1.8921	6.91	47500	2.5883
1.8918	6.99	48000	2.5792
1.8671	7.06	48500	2.6041
1.8551	7.13	49000	2.6070
1.8555	7.2	49500	2.6148
1.8543	7.28	50000	2.6077
1.8485	7.35	50500	2.6131
1.8474	7.42	51000	2.6039
1.8474	7.5	51500	2.5973
1.8442	7.57	52000	2.5946
1.8329	7.64	52500	2.6069
1.8551	7.71	53000	2.5923
1.8433	7.79	53500	2.5922
1.851	7.86	54000	2.5993
1.8313	7.93	54500	2.5960
1.8298	8.0	55000	2.6058
1.8159	8.08	55500	2.6286
1.817	8.15	56000	2.6348
1.8066	8.22	56500	2.6411
1.7935	8.3	57000	2.6338
1.809	8.37	57500	2.6290
1.812	8.44	58000	2.6258
1.79	8.51	58500	2.6321
1.8046	8.59	59000	2.6291
1.7975	8.66	59500	2.6283
1.7968	8.73	60000	2.6284
1.7779	8.81	60500	2.6257
1.7664	8.88	61000	2.6232
1.792	8.95	61500	2.6305
1.7725	9.02	62000	2.6525
1.7563	9.1	62500	2.6794
1.7606	9.17	63000	2.6784
1.7666	9.24	63500	2.6798
1.7551	9.31	64000	2.6813
1.7578	9.39	64500	2.6830
1.7483	9.46	65000	2.6833
1.7431	9.53	65500	2.6884
1.743	9.61	66000	2.6932
1.7395	9.68	66500	2.6927
1.7473	9.75	67000	2.6904
1.7413	9.82	67500	2.6892
1.7437	9.9	68000	2.6898
1.7546	9.97	68500	2.6894

Framework versions

• Transformers 4.21.0.dev0
• Pytorch 1.12.0+cu102
• Datasets 2.3.2
• Tokenizers 0.12.1