clip-roberta-finetuned

davanstrien/clip-roberta-finetuned

clip-roberta-finetuned is a feature extraction model from davanstrien. Use it when you need embeddings to search or compare 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: Feature Extraction
Library: transformers
Model type: vision-text-dual-encoder
Access: Public
Created: Jul 13, 2022
Updated: Jul 15, 2022
SHA: aaf97d71

Datasets

davanstrien/manuscript_noisy_labels_iiif

Model card

From the Hugging Face model README

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

Task: Feature Extraction
Library: transformers
Type: vision-text-dual-encoder
Created: Jul 13, 2022
Updated: Jul 15, 2022