xlm-roberta-targin-final – AI Model by SiddharthaM | AlphaNeural AI

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SiddharthaM/
xlm-roberta-targin-final

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text-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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xlm-roberta-targin-final

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

Loss: 0.8172
Accuracy: 0.6873
Precision: 0.6494
Recall: 0.6422
F1: 0.6450

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: 1e-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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
No log	1.0	296	0.6065	0.6873	0.6537	0.5833	0.5748
0.597	2.0	592	0.5822	0.7015	0.6652	0.6279	0.6332
0.597	3.0	888	0.5704	0.7015	0.6654	0.6551	0.6589
0.5156	4.0	1184	0.6393	0.7044	0.6684	0.6552	0.6597
0.5156	5.0	1480	0.5924	0.7082	0.6752	0.6720	0.6735
0.4479	6.0	1776	0.7029	0.7006	0.6629	0.6351	0.6408
0.3783	7.0	2072	0.6963	0.7072	0.6715	0.6554	0.6606
0.3783	8.0	2368	0.7636	0.6987	0.6627	0.6549	0.6579
0.3253	9.0	2664	0.7804	0.6901	0.6549	0.6523	0.6535
0.3253	10.0	2960	0.8172	0.6873	0.6494	0.6422	0.6450

Framework versions

Transformers 4.24.0.dev0
Pytorch 1.11.0+cu102
Datasets 2.6.1
Tokenizers 0.13.1