roberta-large_ALL_BCE_translated_data_multihead_19_shuffled_special_tokens_final

SotirisLegkas/roberta-large_ALL_BCE_translated_data_multihead_19_shuffled_special_tokens_final

roberta-large_ALL_BCE_translated_data_multihead_19_shuffled_special_tokens_final is a machine learning model from SotirisLegkas. Use it for the machine learning task on the model card, and read the license before you ship it in a product. 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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At a glance

Library: transformers
License: mit
Model type: roberta
Access: Public
Created: May 5, 2024
Updated: May 5, 2024
SHA: c9c717a3

Base models

Model card

From the Hugging Face model README

roberta-large_ALL_BCE_translated_data_multihead_19_shuffled_special_tokens_final

This model is a fine-tuned version of roberta-large on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.4623
F1 Macro 0.1: 0.1265
F1 Macro 0.15: 0.1685
F1 Macro 0.2: 0.2066
F1 Macro 0.25: 0.2414
F1 Macro 0.3: 0.2734
F1 Macro 0.35: 0.3042
F1 Macro 0.4: 0.3334
F1 Macro 0.45: 0.3608
F1 Macro 0.5: 0.3883
F1 Macro 0.55: 0.4138
F1 Macro 0.6: 0.4399
F1 Macro 0.65: 0.4650
F1 Macro 0.7: 0.4898
F1 Macro 0.75: 0.5148
F1 Macro 0.8: 0.5380
F1 Macro 0.85: 0.5589
F1 Macro 0.9: 0.5675
F1 Macro 0.95: 0.5240
Threshold 0: 0.9
Threshold 1: 0.85
Threshold 2: 0.9
Threshold 3: 0.95
Threshold 4: 0.85
Threshold 5: 0.85
Threshold 6: 0.9
Threshold 7: 0.9
Threshold 8: 0.9
Threshold 9: 0.8
Threshold 10: 0.95
Threshold 11: 0.85
Threshold 12: 0.9
Threshold 13: 0.95
Threshold 14: 0.9
Threshold 15: 0.9
Threshold 16: 0.9
Threshold 17: 0.95
Threshold 18: 0.95
0: 0.4528
1: 0.4559
2: 0.5380
3: 0.6832
4: 0.5859
5: 0.5963
6: 0.6048
7: 0.5750
8: 0.6180
9: 0.6267
10: 0.7001
11: 0.6508
12: 0.4796
13: 0.3492
14: 0.5842
15: 0.5477
16: 0.5863
17: 0.7601
18: 0.5831
Max F1: 0.5675
Mean F1: 0.5778

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-06
train_batch_size: 8
eval_batch_size: 8
seed: 2024
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 3
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	F1 Macro 0.1	F1 Macro 0.15	F1 Macro 0.2	F1 Macro 0.25	F1 Macro 0.3	F1 Macro 0.35	F1 Macro 0.4	F1 Macro 0.45	F1 Macro 0.5	F1 Macro 0.55	F1 Macro 0.6	F1 Macro 0.65	F1 Macro 0.7	F1 Macro 0.75	F1 Macro 0.8	F1 Macro 0.85	F1 Macro 0.9	F1 Macro 0.95	Threshold 0	Threshold 1	Threshold 2	Threshold 3	Threshold 4	Threshold 5	Threshold 6	Threshold 7	Threshold 8	Threshold 9	Threshold 10	Threshold 11	Threshold 12	Threshold 13	Threshold 14	Threshold 15	Threshold 16	Threshold 17	Threshold 18	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	Max F1	Mean F1
1.0816	1.0	7458	0.7590	0.0866	0.1168	0.1480	0.1784	0.2078	0.2358	0.2614	0.2862	0.3099	0.3346	0.3563	0.3779	0.3945	0.4110	0.4162	0.4117	0.3749	0.2469	0.75	0.75	0.8	0.8	0.75	0.85	0.85	0.8	0.85	0.75	0.9	0.8	0.85	0.75	0.9	0.85	0.75	0.9	0.8	0.2220	0.2864	0.3679	0.4359	0.4629	0.4666	0.5148	0.3813	0.4581	0.5339	0.5427	0.5521	0.3009	0.2251	0.4403	0.3585	0.4618	0.6699	0.4129	0.4162	0.4260
0.7345	2.0	14916	0.5285	0.1075	0.1419	0.1746	0.2053	0.2342	0.2613	0.2880	0.3140	0.3397	0.3643	0.3895	0.4136	0.4384	0.4637	0.4882	0.5089	0.5194	0.4806	0.9	0.85	0.9	0.95	0.85	0.9	0.9	0.9	0.9	0.8	0.95	0.9	0.9	0.95	0.9	0.95	0.9	0.95	0.95	0.3721	0.4047	0.4824	0.6195	0.5458	0.5504	0.5722	0.5126	0.5609	0.6075	0.6467	0.6181	0.4059	0.3256	0.5522	0.4702	0.5553	0.7329	0.5234	0.5194	0.5294
0.5782	3.0	22374	0.4623	0.1265	0.1685	0.2066	0.2414	0.2734	0.3042	0.3334	0.3608	0.3883	0.4138	0.4399	0.4650	0.4898	0.5148	0.5380	0.5589	0.5675	0.5240	0.9	0.85	0.9	0.95	0.85	0.85	0.9	0.9	0.9	0.8	0.95	0.85	0.9	0.95	0.9	0.9	0.9	0.95	0.95	0.4528	0.4559	0.5380	0.6832	0.5859	0.5963	0.6048	0.5750	0.6180	0.6267	0.7001	0.6508	0.4796	0.3492	0.5842	0.5477	0.5863	0.7601	0.5831	0.5675	0.5778

Framework versions

Transformers 4.39.3
Pytorch 2.2.2+cu121
Datasets 2.18.0
Tokenizers 0.15.2

Library: transformers
Type: roberta
License: mit
Created: May 5, 2024
Updated: May 5, 2024