results

dmishra/results

results is a text classification model from dmishra. 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.

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

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Hugging Face

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At a glance

Task: Text Classification
Library: transformers
License: mit
Model type: roberta
Access: Public
Created: Feb 15, 2023
Updated: Feb 15, 2023
SHA: be2a7330

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Model card

From the Hugging Face model README

results

This model is a fine-tuned version of roberta-base on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.7398
F1: 0.3764
Recall: 0.4257
Precision: 0.5363
Accuracy: 0.7155

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: 0.0001
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Recall	Precision	Accuracy
1.4981	0.03	10	1.4740	0.1556	0.2	0.1274	0.6368
1.473	0.05	20	1.4299	0.1556	0.2	0.1274	0.6368
1.4055	0.08	30	1.3260	0.1556	0.2	0.1274	0.6368
1.2743	0.11	40	1.1795	0.1556	0.2	0.1274	0.6368
1.2937	0.13	50	1.1463	0.1556	0.2	0.1274	0.6368
1.193	0.16	60	1.1367	0.1556	0.2	0.1274	0.6368
1.1709	0.18	70	1.0957	0.1556	0.2	0.1274	0.6368
1.0956	0.21	80	1.0457	0.1556	0.2	0.1274	0.6368
1.0756	0.24	90	0.9852	0.1556	0.2	0.1274	0.6368
0.862	0.26	100	1.0718	0.1556	0.2	0.1274	0.6368
0.9677	0.29	110	0.8931	0.1556	0.2	0.1274	0.6368
0.7597	0.32	120	0.8502	0.2195	0.2499	0.2034	0.6561
0.7797	0.34	130	0.8028	0.2818	0.3758	0.3547	0.6888
0.8114	0.37	140	0.9480	0.2918	0.3313	0.3515	0.6907
0.8693	0.4	150	0.7799	0.3703	0.4000	0.5420	0.7081
0.9561	0.42	160	0.7844	0.3825	0.4261	0.3493	0.7199
0.6979	0.45	170	0.7656	0.3882	0.4404	0.4166	0.7165
0.8083	0.47	180	0.8847	0.3596	0.3688	0.5985	0.7051
0.8009	0.5	190	0.7665	0.3244	0.3916	0.3801	0.7021
0.6833	0.53	200	0.8408	0.4270	0.4631	0.4603	0.6724
0.749	0.55	210	0.7344	0.3889	0.4745	0.4888	0.7120
0.7106	0.58	220	0.7037	0.4511	0.4965	0.4562	0.7343
0.7631	0.61	230	0.7118	0.4331	0.4626	0.4472	0.7378
0.7672	0.63	240	0.6925	0.5035	0.4976	0.5470	0.7412
0.7662	0.66	250	0.7188	0.4425	0.4812	0.4662	0.7308
0.726	0.69	260	0.7120	0.4052	0.4616	0.4508	0.7353
0.9073	0.71	270	0.7969	0.3495	0.3862	0.3547	0.7036
0.6709	0.74	280	0.7429	0.3800	0.4279	0.4814	0.7145
0.9403	0.77	290	0.8199	0.3926	0.3870	0.5164	0.6992
0.9277	0.79	300	0.7304	0.3599	0.4349	0.3786	0.7086
0.9503	0.82	310	0.7764	0.4613	0.5283	0.4263	0.6828
0.6553	0.84	320	0.7386	0.4051	0.4593	0.5329	0.7081
0.7655	0.87	330	0.7527	0.5087	0.5252	0.5375	0.7204
0.6663	0.9	340	0.7248	0.4618	0.4841	0.5283	0.7353
0.915	0.92	350	0.8947	0.4279	0.4875	0.3979	0.6596
0.8718	0.95	360	0.7893	0.3796	0.3938	0.5016	0.7110
0.8338	0.98	370	0.7293	0.4124	0.4277	0.4147	0.7130

Framework versions

Transformers 4.26.1
Pytorch 1.13.1+cu116
Datasets 2.9.0
Tokenizers 0.13.2

Task: Text Classification
Library: transformers
Type: roberta
License: mit
Created: Feb 15, 2023
Updated: Feb 15, 2023