distilroberta-finetuned-wtner

Heem/distilroberta-finetuned-wtner

distilroberta-finetuned-wtner is a machine learning model from Heem. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.

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

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Updated Jul 29, 2022

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At a glance

License: apache-2.0
Access: Public
Created: Jul 28, 2022
Updated: Jul 29, 2022
SHA: 31e561a8

Model card

From the Hugging Face model README

Heem/distilroberta-finetuned-wtner

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

Train Loss: 0.0055
Validation Loss: 0.4521
Train Precision: 0.7410
Train Recall: 0.8122
Train F1: 0.775
Train Accuracy: 0.9382
Epoch: 69

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:

optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2030, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
training_precision: float32

Training results

Train Loss	Validation Loss	Train Precision	Train Recall	Train F1	Train Accuracy	Epoch
1.3579	0.8909	0.0	0.0	0.0	0.7744	0
0.7332	0.6231	0.3526	0.2926	0.3198	0.8256	1
0.5037	0.4471	0.3927	0.3755	0.3839	0.8575	2
0.3675	0.3776	0.484	0.5284	0.5052	0.8855	3
0.2890	0.3519	0.5149	0.6026	0.5553	0.9039	4
0.2367	0.3317	0.5820	0.6507	0.6144	0.9150	5
0.1942	0.2970	0.6220	0.6900	0.6542	0.9237	6
0.1599	0.3040	0.6375	0.6681	0.6525	0.9217	7
0.1281	0.3037	0.6774	0.7336	0.7044	0.9304	8
0.1097	0.3127	0.708	0.7729	0.7390	0.9309	9
0.0915	0.3114	0.6836	0.7642	0.7216	0.9290	10
0.0765	0.3190	0.7072	0.8122	0.7561	0.9372	11
0.0665	0.3169	0.7154	0.7904	0.7510	0.9353	12
0.0543	0.3251	0.7059	0.7860	0.7438	0.9329	13
0.0472	0.3307	0.7181	0.8122	0.7623	0.9357	14
0.0427	0.3639	0.7148	0.7991	0.7546	0.9357	15
0.0380	0.3373	0.7373	0.8210	0.7769	0.9377	16
0.0380	0.3422	0.7449	0.8035	0.7731	0.9372	17
0.0304	0.3455	0.7530	0.8122	0.7815	0.9386	18
0.0271	0.3584	0.7294	0.8122	0.7686	0.9377	19
0.0249	0.3661	0.7291	0.7991	0.7625	0.9377	20
0.0205	0.3683	0.7352	0.8122	0.7718	0.9391	21
0.0212	0.3855	0.7331	0.8035	0.7667	0.9382	22
0.0188	0.3814	0.7419	0.8035	0.7715	0.9391	23
0.0189	0.3889	0.7352	0.8122	0.7718	0.9357	24
0.0161	0.3913	0.7379	0.7991	0.7673	0.9382	25
0.0154	0.3872	0.7470	0.8122	0.7782	0.9406	26
0.0144	0.3934	0.7326	0.8253	0.7762	0.9401	27
0.0154	0.4167	0.7255	0.8079	0.7645	0.9343	28
0.0135	0.3976	0.7341	0.8079	0.7692	0.9362	29
0.0119	0.4118	0.7510	0.8297	0.7884	0.9382	30
0.0103	0.4112	0.7323	0.8122	0.7702	0.9372	31
0.0103	0.4172	0.7362	0.8166	0.7743	0.9382	32
0.0111	0.4157	0.7283	0.8079	0.7660	0.9382	33
0.0103	0.4152	0.7262	0.7991	0.7609	0.9372	34
0.0117	0.4090	0.7188	0.8035	0.7588	0.9377	35
0.0098	0.4268	0.7302	0.8035	0.7651	0.9367	36
0.0082	0.4354	0.7233	0.7991	0.7593	0.9362	37
0.0096	0.4298	0.7154	0.7904	0.7510	0.9357	38
0.0093	0.4294	0.7273	0.8035	0.7635	0.9362	39
0.0084	0.4266	0.7298	0.7904	0.7589	0.9348	40
0.0076	0.4230	0.7251	0.7948	0.7583	0.9357	41
0.0068	0.4243	0.7075	0.7817	0.7427	0.9329	42
0.0080	0.4379	0.7137	0.7729	0.7421	0.9338	43
0.0067	0.4361	0.7302	0.8035	0.7651	0.9362	44
0.0066	0.4377	0.7341	0.8079	0.7692	0.9367	45
0.0056	0.4357	0.7222	0.7948	0.7568	0.9362	46
0.0060	0.4393	0.7205	0.7991	0.7578	0.9362	47
0.0060	0.4429	0.7194	0.7948	0.7552	0.9357	48
0.0054	0.4416	0.7312	0.8079	0.7676	0.9367	49
0.0060	0.4413	0.7188	0.8035	0.7588	0.9362	50
0.0058	0.4381	0.7344	0.8210	0.7753	0.9377	51
0.0063	0.4388	0.7309	0.7948	0.7615	0.9377	52
0.0057	0.4402	0.7412	0.8253	0.7810	0.9382	53
0.0052	0.4381	0.7362	0.8166	0.7743	0.9377	54
0.0049	0.4407	0.7362	0.8166	0.7743	0.9377	55
0.0050	0.4394	0.7490	0.8210	0.7833	0.9386	56
0.0047	0.4481	0.7460	0.8210	0.7817	0.9382	57
0.0052	0.4544	0.748	0.8166	0.7808	0.9367	58
0.0049	0.4501	0.7430	0.8079	0.7741	0.9362	59
0.0050	0.4504	0.744	0.8122	0.7766	0.9367	60
0.0047	0.4517	0.7312	0.8079	0.7676	0.9372	61
0.0049	0.4526	0.7450	0.8166	0.7792	0.9382	62
0.0049	0.4534	0.7490	0.8210	0.7833	0.9386	63
0.0056	0.4543	0.748	0.8166	0.7808	0.9386	64
0.0044	0.4522	0.7410	0.8122	0.775	0.9382	65
0.0047	0.4522	0.7410	0.8122	0.775	0.9382	66
0.0050	0.4521	0.7410	0.8122	0.775	0.9382	67
0.0049	0.4521	0.7410	0.8122	0.775	0.9382	68
0.0055	0.4521	0.7410	0.8122	0.775	0.9382	69

Framework versions

Transformers 4.20.1
TensorFlow 2.9.1
Datasets 2.4.0
Tokenizers 0.12.1

License: apache-2.0
Created: Jul 28, 2022
Updated: Jul 29, 2022