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CNEC2_0_Supertypes_xlm-roberta-large

Token Classification

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CNEC2_0_Supertypes_xlm-roberta-large

This model is a fine-tuned version of FacebookAI/xlm-roberta-large on the cnec dataset. It achieves the following results on the evaluation set:

Loss: 0.2155
Precision: 0.8565
Recall: 0.8971
F1: 0.8763
Accuracy: 0.9709

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: 2e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.4393	1.0	900	0.1671	0.7756	0.8195	0.7969	0.9590
0.1716	2.0	1800	0.1409	0.8155	0.8583	0.8364	0.9662
0.1326	3.0	2700	0.1288	0.8203	0.8748	0.8467	0.9687
0.1027	4.0	3600	0.1408	0.8290	0.8732	0.8505	0.9683
0.0891	5.0	4500	0.1447	0.8485	0.9000	0.8735	0.9725
0.0715	6.0	5400	0.1393	0.8561	0.8868	0.8712	0.9713
0.0644	7.0	6300	0.1586	0.8517	0.8918	0.8713	0.9702
0.0535	8.0	7200	0.1526	0.8481	0.8810	0.8643	0.9696
0.0492	9.0	8100	0.1795	0.8529	0.8984	0.8751	0.9702
0.0391	10.0	9000	0.1903	0.8536	0.8938	0.8733	0.9693
0.0323	11.0	9900	0.1885	0.8615	0.9046	0.8825	0.9724
0.0274	12.0	10800	0.2099	0.8585	0.9025	0.8800	0.9696
0.0237	13.0	11700	0.1944	0.8624	0.9009	0.8812	0.9720
0.0245	14.0	12600	0.2129	0.8618	0.8967	0.8789	0.9711
0.0206	15.0	13500	0.2155	0.8565	0.8971	0.8763	0.9709

Framework versions

Transformers 4.36.2
Pytorch 2.1.2+cu121
Datasets 2.16.1
Tokenizers 0.15.0

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Model size

559M params

Tensor type

F32

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Base model

FacebookAI/xlm-roberta-large

Finetuned

(287)

this model

Evaluation results

Precision on cnec
validation set self-reported

0.856
Recall on cnec
validation set self-reported

0.897
F1 on cnec
validation set self-reported

0.876
Accuracy on cnec
validation set self-reported

0.971

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