xlmr-large-AmDi-synset-classifier-pair – AI Model by kugler | AlphaNeural AI

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kugler/
xlmr-large-AmDi-synset-classifier-pair

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text-classification

generated_from_trainer

FacebookAI/xlm-roberta-large

autotrain_compatible

endpoints_compatible

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xlmr_synset_classifier_pair

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

Loss: 0.4819
Accuracy: 0.8520
F1: 0.8428
Precision: 0.8474
Recall: 0.8520
F1 Macro: 0.6767
Precision Macro: 0.6791
Recall Macro: 0.6942
F1 Micro: 0.8520
Precision Micro: 0.8520
Recall Micro: 0.8520

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: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 50
num_epochs: 5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall	F1 Macro	Precision Macro	Recall Macro	F1 Micro	Precision Micro	Recall Micro
3.5852	0.6221	100	2.1752	0.5101	0.4039	0.3639	0.5101	0.1413	0.1482	0.1624	0.5101	0.5101	0.5101
1.332	1.2442	200	0.7470	0.7936	0.7645	0.7729	0.7936	0.4907	0.4864	0.5309	0.7936	0.7936	0.7936
0.7141	1.8663	300	0.5803	0.8291	0.8145	0.8311	0.8291	0.5873	0.5801	0.6215	0.8291	0.8291	0.8291
0.5558	2.4883	400	0.5548	0.8398	0.8264	0.8336	0.8398	0.6228	0.6142	0.6467	0.8398	0.8398	0.8398
0.5017	3.1104	500	0.5036	0.8448	0.8315	0.8398	0.8448	0.6279	0.6192	0.6553	0.8448	0.8448	0.8448
0.4131	3.7325	600	0.4765	0.8457	0.8365	0.8368	0.8457	0.6435	0.6397	0.6586	0.8457	0.8457	0.8457
0.3848	4.3546	700	0.4845	0.8497	0.8398	0.8457	0.8497	0.6779	0.6796	0.6972	0.8497	0.8497	0.8497
0.3476	4.9767	800	0.4819	0.8520	0.8428	0.8474	0.8520	0.6767	0.6791	0.6942	0.8520	0.8520	0.8520

Framework versions

Transformers 4.45.2
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.20.3