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xlm-roberta-finetuned-financial-news-sentiment-analysis-european – AI Model by nojedag | AlphaNeural AI
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xlm-roberta-finetuned-financial-news-sentiment-analysis-european
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transformers
tensorboard
safetensors
xlm-roberta
text-classification
generated_from_trainer
en
es
fr
de
nojedag/financial_phrasebank_multilingual_augmented
FacebookAI/xlm-roberta-base
finetune
mit
autotrain_compatible
endpoints_compatible
us
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xlm-roberta-finetuned-financial-news-sentiment-analysis-european
This model is a fine-tuned version of
FacebookAI/xlm-roberta-base
on an unknown dataset. It achieves the following results on the evaluation set:
eval_loss: 0.4898
eval_model_preparation_time: 0.0035
eval_accuracy: 0.8563
eval_macro_precision: 0.8561
eval_macro_recall: 0.8700
eval_macro_f1: 0.8586
eval_neutral_precision: 0.9346
eval_neutral_recall: 0.7894
eval_neutral_f1: 0.8559
eval_positive_precision: 0.8988
eval_positive_recall: 0.9350
eval_positive_f1: 0.9165
eval_negative_precision: 0.7350
eval_negative_recall: 0.8858
eval_negative_f1: 0.8034
eval_runtime: 28.86
eval_samples_per_second: 287.942
eval_steps_per_second: 18.018
step: 0
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
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 846
num_epochs: 7
mixed_precision_training: Native AMP
Framework versions
Transformers 4.51.3
Pytorch 2.7.0+cu128
Datasets 3.6.0
Tokenizers 0.21.1
