scenario-TCR-data-AmazonScience-massive-all_1.1-model-xlm-roberta-base – AI Model by haryoaw | AlphaNeural AI

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haryoaw/
scenario-TCR-data-AmazonScience-massive-all_1.1-model-xlm-roberta-base

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scenario-TCR-data-AmazonScience-massive-all_1.1-model-xlm-roberta-base

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

Loss: 0.8546
Accuracy: 0.8443
F1: 0.8187

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-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 500

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.6138	0.27	5000	0.6955	0.8197	0.7723
0.4705	0.53	10000	0.6878	0.8280	0.7855
0.4019	0.8	15000	0.6850	0.8368	0.8005
0.2929	1.07	20000	0.7296	0.8316	0.7965
0.3011	1.34	25000	0.7140	0.8426	0.8139
0.2921	1.6	30000	0.7252	0.8418	0.8152
0.2799	1.87	35000	0.7186	0.8431	0.8152
0.2103	2.14	40000	0.7557	0.8462	0.8172
0.2251	2.41	45000	0.7926	0.8411	0.8095
0.2118	2.67	50000	0.7915	0.8427	0.8126
0.2239	2.94	55000	0.7813	0.8416	0.8076
0.1727	3.21	60000	0.8273	0.8471	0.8224
0.1785	3.47	65000	0.8192	0.8447	0.8149
0.2008	3.74	70000	0.8043	0.8464	0.8213
0.1773	4.01	75000	0.8555	0.8407	0.8121
0.165	4.28	80000	0.8556	0.8456	0.8218
0.1658	4.54	85000	0.8546	0.8443	0.8187

Framework versions

Transformers 4.36.2
Pytorch 2.1.2
Datasets 2.16.0
Tokenizers 0.15.0