iva_mt-leyzer-slot_baseline-xlm_r-pl – AI Model by cartesinus | AlphaNeural AI

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cartesinus/
iva_mt-leyzer-slot_baseline-xlm_r-pl

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token-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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fedcsis_translated-slot_baseline-xlm_r-pl

This model is a fine-tuned version of xlm-roberta-base on the leyzer-fedcsis-translated dataset.

Results on untranslated test set:

Precision: 0.5909
Recall: 0.5766
F1: 0.5836
Accuracy: 0.7484

It achieves the following results on the evaluation set:

Loss: 1.0761
Precision: 0.7299
Recall: 0.7427
F1: 0.7363
Accuracy: 0.8415

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: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
1.4842	1.0	814	0.7712	0.5858	0.6026	0.5941	0.7918
0.5128	2.0	1628	0.6435	0.6469	0.6828	0.6644	0.8119
0.3526	3.0	2442	0.7030	0.6823	0.7045	0.6933	0.8242
0.2142	4.0	3256	0.7695	0.7112	0.7243	0.7177	0.8381
0.1422	5.0	4070	0.8550	0.7203	0.7310	0.7256	0.8399
0.1188	6.0	4884	0.9209	0.7183	0.7333	0.7258	0.8391
0.0915	7.0	5698	0.9892	0.7238	0.7372	0.7305	0.8404
0.072	8.0	6512	1.0271	0.7230	0.7364	0.7296	0.8417
0.0626	9.0	7326	1.0608	0.7312	0.7417	0.7364	0.8419
0.0613	10.0	8140	1.0761	0.7299	0.7427	0.7363	0.8415

Framework versions

Transformers 4.27.3
Pytorch 1.13.1+cu116
Datasets 2.10.1
Tokenizers 0.13.2